Tag Archives: medicine

Attacker Causes Epileptic Seizure over the Internet

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/12/attacker_causes.html

This isn’t a first, but I think it will be the first conviction:

The GIF set off a highly unusual court battle that is expected to equip those in similar circumstances with a new tool for battling threatening trolls and cyberbullies. On Monday, the man who sent Eichenwald the moving image, John Rayne Rivello, was set to appear in a Dallas County district court. A last-minute rescheduling delayed the proceeding until Jan. 31, but Rivello is still expected to plead guilty to aggravated assault. And he may be the first of many.

The Epilepsy Foundation announced on Monday it lodged a sweeping slate of criminal complaints against a legion of copycats who targeted people with epilepsy and sent them an onslaught of strobe GIFs — a frightening phenomenon that unfolded in a short period of time during the organization’s marking of National Epilepsy Awareness Month in November.

[…]

Rivello’s supporters — among them, neo-Nazis and white nationalists, including Richard Spencer — have also argued that the issue is about freedom of speech. But in an amicus brief to the criminal case, the First Amendment Clinic at Duke University School of Law argued Rivello’s actions were not constitutionally protected.

“A brawler who tattoos a message onto his knuckles does not throw every punch with the weight of First Amendment protection behind him,” the brief stated. “Conduct like this does not constitute speech, nor should it. A deliberate attempt to cause physical injury to someone does not come close to the expression which the First Amendment is designed to protect.”

Another article.

EDITED TO ADD(12/19): More articles.

Cardiac Biometric

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/07/cardiac_biometr.html

MIT Technology Review is reporting about an infrared laser device that can identify people by their unique cardiac signature at a distance:

A new device, developed for the Pentagon after US Special Forces requested it, can identify people without seeing their face: instead it detects their unique cardiac signature with an infrared laser. While it works at 200 meters (219 yards), longer distances could be possible with a better laser. “I don’t want to say you could do it from space,” says Steward Remaly, of the Pentagon’s Combatting Terrorism Technical Support Office, “but longer ranges should be possible.”

Contact infrared sensors are often used to automatically record a patient’s pulse. They work by detecting the changes in reflection of infrared light caused by blood flow. By contrast, the new device, called Jetson, uses a technique known as laser vibrometry to detect the surface movement caused by the heartbeat. This works though typical clothing like a shirt and a jacket (though not thicker clothing such as a winter coat).

[…]

Remaly’s team then developed algorithms capable of extracting a cardiac signature from the laser signals. He claims that Jetson can achieve over 95% accuracy under good conditions, and this might be further improved. In practice, it’s likely that Jetson would be used alongside facial recognition or other identification methods.

Wenyao Xu of the State University of New York at Buffalo has also developed a remote cardiac sensor, although it works only up to 20 meters away and uses radar. He believes the cardiac approach is far more robust than facial recognition. “Compared with face, cardiac biometrics are more stable and can reach more than 98% accuracy,” he says.

I have my usual questions about false positives vs false negatives, how stable the biometric is over time, and whether it works better or worse against particular sub-populations. But interesting nonetheless.

Fake News and Pandemics

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/06/fake_news_and_p.html

When the next pandemic strikes, we’ll be fighting it on two fronts. The first is the one you immediately think about: understanding the disease, researching a cure and inoculating the population. The second is new, and one you might not have thought much about: fighting the deluge of rumors, misinformation and flat-out lies that will appear on the internet.

The second battle will be like the Russian disinformation campaigns during the 2016 presidential election, only with the addition of a deadly health crisis and possibly without a malicious government actor. But while the two problems — misinformation affecting democracy and misinformation affecting public health — will have similar solutions, the latter is much less political. If we work to solve the pandemic disinformation problem, any solutions are likely to also be applicable to the democracy one.

Pandemics are part of our future. They might be like the 1968 Hong Kong flu, which killed a million people, or the 1918 Spanish flu, which killed over 40 million. Yes, modern medicine makes pandemics less likely and less deadly. But global travel and trade, increased population density, decreased wildlife habitats, and increased animal farming to satisfy a growing and more affluent population have made them more likely. Experts agree that it’s not a matter of if — it’s only a matter of when.

When the next pandemic strikes, accurate information will be just as important as effective treatments. We saw this in 2014, when the Nigerian government managed to contain a subcontinentwide Ebola epidemic to just 20 infections and eight fatalities. Part of that success was because of the ways officials communicated health information to all Nigerians, using government-sponsored videos, social media campaigns and international experts. Without that, the death toll in Lagos, a city of 21 million people, would have probably been greater than the 11,000 the rest of the continent experienced.

There’s every reason to expect misinformation to be rampant during a pandemic. In the early hours and days, information will be scant and rumors will abound. Most of us are not health professionals or scientists. We won’t be able to tell fact from fiction. Even worse, we’ll be scared. Our brains work differently when we are scared, and they latch on to whatever makes us feel safer — even if it’s not true.

Rumors and misinformation could easily overwhelm legitimate news channels, as people share tweets, images and videos. Much of it will be well-intentioned but wrong — like the misinformation spread by the anti-vaccination community today ­– but some of it may be malicious. In the 1980s, the KGB ran a sophisticated disinformation campaign ­– Operation Infektion ­– to spread the rumor that HIV/AIDS was a result of an American biological weapon gone awry. It’s reasonable to assume some group or country would deliberately spread intentional lies in an attempt to increase death and chaos.

It’s not just misinformation about which treatments work (and are safe), and which treatments don’t work (and are unsafe). Misinformation can affect society’s ability to deal with a pandemic at many different levels. Right now, Ebola relief efforts in the Democratic Republic of Congo are being stymied by mistrust of health workers and government officials.

It doesn’t take much to imagine how this can lead to disaster. Jay Walker, curator of the TEDMED conferences, laid out some of the possibilities in a 2016 essay: people overwhelming and even looting pharmacies trying to get some drug that is irrelevant or nonexistent, people needlessly fleeing cities and leaving them paralyzed, health workers not showing up for work, truck drivers and other essential people being afraid to enter infected areas, official sites like CDC.gov being hacked and discredited. This kind of thing can magnify the health effects of a pandemic many times over, and in extreme cases could lead to a total societal collapse.

This is going to be something that government health organizations, medical professionals, social media companies and the traditional media are going to have to work out together. There isn’t any single solution; it will require many different interventions that will all need to work together. The interventions will look a lot like what we’re already talking about with regard to government-run and other information influence campaigns that target our democratic processes: methods of visibly identifying false stories, the identification and deletion of fake posts and accounts, ways to promote official and accurate news, and so on. At the scale these are needed, they will have to be done automatically and in real time.

Since the 2016 presidential election, we have been talking about propaganda campaigns, and about how social media amplifies fake news and allows damaging messages to spread easily. It’s a hard discussion to have in today’s hyperpolarized political climate. After any election, the winning side has every incentive to downplay the role of fake news.

But pandemics are different; there’s no political constituency in favor of people dying because of misinformation. Google doesn’t want the results of peoples’ well-intentioned searches to lead to fatalities. Facebook and Twitter don’t want people on their platforms sharing misinformation that will result in either individual or mass deaths. Focusing on pandemics gives us an apolitical way to collectively approach the general problem of misinformation and fake news. And any solutions for pandemics are likely to also be applicable to the more general ­– and more political ­– problems.

Pandemics are inevitable. Bioterror is already possible, and will only get easier as the requisite technologies become cheaper and more common. We’re experiencing the largest measles outbreak in 25 years thanks to the anti-vaccination movement, which has hijacked social media to amplify its messages; we seem unable to beat back the disinformation and pseudoscience surrounding the vaccine. Those same forces will dramatically increase death and social upheaval in the event of a pandemic.

Let the Russian propaganda attacks on the 2016 election serve as a wake-up call for this and other threats. We need to solve the problem of misinformation during pandemics together –­ governments and industries in collaboration with medical officials, all across the world ­– before there’s a crisis. And the solutions will also help us shore up our democracy in the process.

This essay previously appeared in the New York Times.

Maliciously Tampering with Medical Imagery

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/04/maliciously_tam.html

In what I am sure is only a first in many similar demonstrations, researchers are able to add or remove cancer signs from CT scans. The results easily fool radiologists.

I don’t think the medical device industry has thought at all about data integrity and authentication issues. In a world where sensor data of all kinds is undetectably manipulatable, they’re going to have to start.

Research paper. Slashdot thread.

Poor Security at the UK National Health Service

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/02/poor_security_a.html

The Guardian is reporting that “every NHS trust assessed for cyber security vulnerabilities has failed to meet the standard required.”

This is the same NHS that was debilitated by WannaCry.

EDITED TO ADD (2/13): More news.

And don’t think that US hospitals are much better.

Community Profile: Dr. Lucy Rogers

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/community-profile-lucy-rogers/

This column is from The MagPi issue 58. You can download a PDF of the full issue for free, or subscribe to receive the print edition through your letterbox or the digital edition on your tablet. All proceeds from the print and digital editions help the Raspberry Pi Foundation achieve our charitable goals.

Dr Lucy Rogers calls herself a Transformer. “I transform simple electronics into cool gadgets, I transform science into plain English, I transform problems into opportunities. I am also a catalyst. I am interested in everything around me, and can often see ways of putting two ideas from very different fields together into one package. If I cannot do this myself, I connect the people who can.”

Dr Lucy Rogers Raspberry Pi The MagPi Community Profile

Among many other projects, Dr Lucy Rogers currently focuses much of her attention on reducing the damage from space debris

It’s a pretty wide range of interests and skills for sure. But it only takes a brief look at Lucy’s résumé to realise that she means it. When she says she’s interested in everything around her, this interest reaches from electronics to engineering, wearable tech, space, robotics, and robotic dinosaurs. And she can be seen talking about all of these things across various companies’ social media, such as IBM, websites including the Women’s Engineering Society, and books, including her own.

Dr Lucy Rogers Raspberry Pi The MagPi Community Profile

With her bright LED boots, Lucy was one of the wonderful Pi community members invited to join us and HRH The Duke of York at St James’s Palace just over a year ago

When not attending conferences as guest speaker, tinkering with electronics, or creating engaging IoT tutorials, she can be found retrofitting Raspberry Pis into the aforementioned robotic dinosaurs at Blackgang Chine Land of Imagination, writing, and judging battling bots for the BBC’s Robot Wars.

Dr Lucy Rogers Raspberry Pi The MagPi Community Profile

First broadcast in the UK between 1998 and 2004, Robot Wars was revived in 2016 with a new look and new judges, including Dr Lucy Rogers. Competitors battle their home-brew robots, and Lucy, together with the other two judges, awards victories among the carnage of robotic remains

Lucy graduated from Lancaster University with a degree in Mechanical Engineering. After that, she spent seven years at Rolls-Royce Industrial Power Group as a graduate trainee before becoming a chartered engineer and earning her PhD in bubbles.

Bubbles?

“Foam formation in low‑expansion fire-fighting equipment. I investigated the equipment to determine how the bubbles were formed,” she explains. Obviously. Bubbles!

Dr Lucy Rogers Raspberry Pi The MagPi Community Profile

Lucy graduated from the Singularity University Graduate Studies Program in 2011, focusing on how robotics, nanotech, medicine, and various technologies can tackle the challenges facing the world

She then went on to become a fellow of the Royal Astronomical Society (RAS) in 2005 and, later, a fellow of both the Institution of Mechanical Engineers (IMechE) and British Interplanetary Society. As a member of the Association of British Science Writers, Lucy wrote It’s ONLY Rocket Science: an Introduction in Plain English.

Dr Lucy Rogers Raspberry Pi The MagPi Community Profile

In It’s Only Rocket Science: An Introduction in Plain English Lucy explains that ‘hard to understand’ isn’t the same as ‘impossible to understand’, and takes her readers through the journey of building a rocket, leaving Earth, and travelling the cosmos

As a standout member of the industry, and all-round fun person to be around, Lucy has quickly established herself as a valued member of the Pi community.

In 2014, with the help of Neil Ford and Andy Stanford-Clark, Lucy worked with the UK’s oldest amusement park, Blackgang Chine Land of Imagination, on the Isle of Wight, with the aim of updating its animatronic dinosaurs. The original Blackgang Chine dinosaurs had a limited range of behaviour: able to roar, move their heads, and stomp a foot in a somewhat repetitive action.

When she contacted Raspberry Pi back in the November of that same year, the team were working on more creative, varied behaviours, giving each dinosaur a new Raspberry Pi-sized brain. This later evolved into a very successful dino-hacking Raspberry Jam.

Dr Lucy Rogers Raspberry Pi The MagPi Community Profile

Lucy, Neil Ford, and Andy Stanford-Clark used several Raspberry Pis and Node-RED to visualise flows of events when updating the robotic dinosaurs at Blackgang Chine. They went on to create the successful WightPi Raspberry Jam event, where visitors could join in with the unique hacking opportunity.

Given her love for tinkering with tech, and a love for stand-up comedy that can be uncovered via a quick YouTube search, it’s no wonder that Lucy was asked to help judge the first round of the ‘Make us laugh’ Pioneers challenge for Raspberry Pi. Alongside comedian Bec Hill, Code Club UK director Maria Quevedo, and the face of the first challenge, Owen Daughtery, Lucy lent her expertise to help name winners in the various categories of the teens event, and offered her support to future Pioneers.

The post Community Profile: Dr. Lucy Rogers appeared first on Raspberry Pi.

Jackpotting Attacks Against US ATMs

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/02/jackpotting_att.html

Brian Krebs is reporting sophisticated jackpotting attacks against US ATMs. The attacker gains physical access to the ATM, plants malware using specialized electronics, and then later returns and forces the machine to dispense all the cash it has inside.

The Secret Service alert explains that the attackers typically use an endoscope — a slender, flexible instrument traditionally used in medicine to give physicians a look inside the human body — to locate the internal portion of the cash machine where they can attach a cord that allows them to sync their laptop with the ATM’s computer.

“Once this is complete, the ATM is controlled by the fraudsters and the ATM will appear Out of Service to potential customers,” reads the confidential Secret Service alert.

At this point, the crook(s) installing the malware will contact co-conspirators who can remotely control the ATMs and force the machines to dispense cash.

“In previous Ploutus.D attacks, the ATM continuously dispensed at a rate of 40 bills every 23 seconds,” the alert continues. Once the dispense cycle starts, the only way to stop it is to press cancel on the keypad. Otherwise, the machine is completely emptied of cash, according to the alert.

Lots of details in the article.

Accelerating Precision Medicine at Scale

Post Syndicated from Chris Munns original https://aws.amazon.com/blogs/compute/accelerating-precision-medicine-at-scale/

This post courtesy of Aaron Friedman, Healthcare and Life Sciences Partner Solutions Architect, AWS and Angel Pizarro, Genomics and Life Sciences Senior Solutions Architect, AWS

 

Precision medicine is tailored to individuals based on quantitative signatures, including genomics, lifestyle, and environment. It is often considered to be the driving force behind the next wave of human health. Through new initiatives and technologies such as population-scale genomics sequencing and IoT-backed wearables, researchers and clinicians in both commercial and public sectors are gaining new, previously inaccessible insights.

Many of these precision medicine initiatives are already happening on AWS. A few of these include:

  • PrecisionFDA – This initiative is led by the US Food and Drug Administration. The goal is to define the next-generation standard of care for genomics in precision medicine.
  • American Heart Association Precision Medicine Platform – The platform gives researchers the ability to collaborate and analyze datasets, to better predict and intervene in cardiovascular disease and stroke.
  • Deloitte ConvergeHEALTH – Gives healthcare and life sciences organizations the ability to analyze their disparate datasets on a singular real world evidence platform.

Central to many of these initiatives is genomics, which gives healthcare organizations the ability to establish a baseline for longitudinal studies. Due to its wide applicability in precision medicine initiatives—from rare disease diagnosis to improving outcomes of clinical trials—genomics data is growing at a larger rate than Moore’s law across the globe. Many expect these datasets to grow to be in the range of tens of exabytes by 2025.

Genomics data is also regularly re-analyzed by the community as researchers develop new computational methods or compare older data with newer genome references. These trends are driving innovations in data analysis methods and algorithms to address the massive increase of computational requirements.

Edico Genome, an AWS Partner Network (APN) Partner, has developed a novel solution that accelerates genomics analysis using field-programmable gate arrays, or FPGAs. Historically, Edico Genome deployed their FPGA appliances on-premises. When AWS announced the Amazon EC2 F1 PGA-based instance family in December 2016, Edico Genome adopted a cloud-first strategy, became a F1 launch partner, and was one of the first partners to deploy FPGA-enabled applications on AWS.

On October 19, 2017, Edico Genome partnered with the Children’s Hospital of Philadelphia (CHOP) to demonstrate their FPGA-accelerated genomic pipeline software, called DRAGEN. It can significantly reduce time-to-insight for patient genomes, and analyzed 1,000 genomes from the Center for Applied Genomics Biobank in the shortest time possible. This set a Guinness World Record for the fastest analysis of 1000 whole human genomes, and they did this using 1000 EC2 f1.2xlarge instances in a single AWS region. Not only were they able to analyze genomes at high throughput, they did so averaging approximately $3 per whole human genome of AWS compute for the analysis.

minutes-dragon-run

The version of DRAGEN that Edico Genome used for this analysis was also the same one used in the precisionFDA Hidden Treasures – Warm Up challenge, where they were one of the top performers in every assessment.

In the remainder of this post, we walk through the architecture used by Edico Genome, combining EC2 F1 instances and AWS Batch to achieve this milestone.

EC2 F1 instances and Edico’s DRAGEN

EC2 F1 instances provide access to programmable hardware-acceleration using FPGAs at a cloud scale. AWS customers use F1 instances for a wide variety of applications, including big data, financial analytics and risk analysis, image and video processing, engineering simulations, AR/VR, and accelerated genomics.
Edico Genome’s FPGA-backed DRAGEN Bio-IT Platform is now integrated with EC2 F1 instances. You can access the accuracy, speed, flexibility, and low compute cost of DRAGEN through a number of third-party platforms, AWS Marketplace, and Edico Genome’s own platform. The DRAGEN platform offers a scalable, accelerated, and cost-efficient secondary analysis solution for a wide variety of genomics applications. Edico Genome also provides a highly optimized mechanism for the efficient storage of genomic data.

Scaling DRAGEN on AWS

Edico Genome used 1,000 EC2 F1 instances to help their customer, the Children’s Hospital of Philadelphia (CHOP), to process and analyze all 1,000 whole human genomes in parallel. They used AWS Batch to provision compute resources and orchestrate DRAGEN compute jobs across the 1,000 EC2 F1 instances. This solution successfully addressed the challenge of creating a scalable genomic processing pipeline that can easily scale to thousands of engines running in parallel.

Architecture

A simplified view of the architecture used for the analysis is shown in the following diagram:
dragen-architecture

  1. DRAGEN’s portal uses Elastic Load Balancing and Auto Scaling groups to scale out EC2 instances that submitted jobs to AWS Batch.
  2. Job metadata is stored in their Workflow Management (WFM) database, built on top of Amazon Aurora.
  3. The DRAGEN Workflow Manager API submits jobs to AWS Batch.
  4. These jobs are executed on the AWS Batch managed compute environment that was responsible for launching the EC2 F1 instances.
  5. These jobs run as Docker containers that have the requisite DRAGEN binaries for whole genome analysis.
  6. As each job runs, it retrieves and stores genomics data that is staged in Amazon S3.

The steps listed previously can also be bucketed into the following higher-level layers:

  • Workflow:  Edico Genome used their Workflow Management API to orchestrate the submission of AWS Batch jobs. Metadata for the jobs (such as the S3 locations of the genomes, etc.) resides in the Workflow Management Database backed by Amazon Aurora.
  • Batch execution:  AWS Batch launches EC2 F1 instances and coordinates the execution of DRAGEN jobs on these compute resources. AWS Batch enabled Edico to quickly and easily scale up to the full number of instances they needed as jobs were submitted. They also scaled back down as each job was completed, to optimize for both cost and performance.
  • Compute/job:  Edico Genome stored their binaries in a Docker container that AWS Batch deployed onto each of the F1 instances, giving each instance the ability to run DRAGEN without the need to pre-install the core executables. The AWS based DRAGEN solution streams all genomics data from S3 for local computation and then writes the results to a destination bucket. They used an AWS Batch job role that specified the IAM permissions. The role ensured that DRAGEN only had access to the buckets or S3 key space it needed for the analysis. Jobs didn’t need to embed AWS credentials.

We also covered these concepts previously in the Building High-Throughput Genomics Batch Workflows on AWS series on the AWS Compute blog.

Walkthrough

In the following sections, we dive deeper into several tasks that enabled Edico Genome’s scalable FPGA genome analysis on AWS:

  1. Prepare your Amazon FPGA Image for AWS Batch
  2. Create a Dockerfile and build your Docker image
  3. Set up your AWS Batch FPGA compute environment

Prerequisites

In brief, you need a modern Linux distribution (3.10+), Amazon ECS Container Agent, awslogs driver, and Docker configured on your image. There are additional recommendations in the Compute Resource AMI specification.

Preparing your Amazon FPGA Image for AWS Batch

You can use any Amazon Machine Image (AMI) or Amazon FPGA Image (AFI) with AWS Batch, provided that it meets the Compute Resource AMI specification. This gives you the ability to customize any workload by increasing the size of root or data volumes, adding instance stores, and connecting with the FPGA (F) and GPU (G and P) instance families.

Next, install the AWS CLI:

pip install awscli

Add any additional software required to interact with the FPGAs on the F1 instances.

As a starting point, AWS publishes an FPGA Developer AMI in the AWS Marketplace. It is based on a CentOS Linux image and includes pre-integrated FPGA development tools. It also includes the runtime tools required to develop and use custom FPGAs for hardware acceleration applications.

For more information about how to set up custom AMIs for your AWS Batch managed compute environments, see Creating a Compute Resource AMI.

Building your Dockerfile

There are two common methods for connecting to AWS Batch to run FPGA-enabled algorithms. The first method, which is the route Edico Genome took, involves storing your binaries in the Docker container itself and running that on top of an F1 instance with Docker installed. The following code example is what a Dockerfile to build your container might look like for this scenario.

# DRAGEN_EXEC Docker image generator --
# Run this Dockerfile from a local directory that contains the latest release of
# - Dragen RPM and Linux DMA Driver available from Edico
# - Edico's Dragen WFMS Wrapper files
FROM centos:centos7
RUN rpm -Uvh https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm
# Install Basic packages needed for Dragen
RUN yum -y install \
perl \
sos \
coreutils \
gdb \
time \
systemd-libs \
bzip2-libs \
R \
ca-certificates \
ipmitool \
smartmontools \
rsync
# Install the Dragen RPM
RUN mkdir -m777 -p /var/log/dragen /var/run/dragen
ADD . /root
RUN rpm -Uvh /root/edico_driver*.rpm || true
RUN rpm -Uvh /root/dragen-aws*.rpm || true
# Auto generate the Dragen license
RUN /opt/edico/bin/dragen_lic -i auto
#########################################################
# Now install the Edico WFMS "Wrapper" functions
# Add development tools needed for some util
RUN yum groupinstall -y "Development Tools"
# Install necessary standard packages
RUN yum -y install \
dstat \
git \
python-devel \
python-pip \
time \
tree && \
pip install --upgrade pip && \
easy_install requests && \
pip install psutil && \
pip install python-dateutil && \
pip install constants && \
easy_install boto3
# Setup Python path used by the wrapper
RUN mkdir -p /opt/workflow/python/bin
RUN ln -s /usr/bin/python /opt/workflow/python/bin/python2.7
RUN ln -s /usr/bin/python /opt/workflow/python/bin/python
# Install d_haul and dragen_job_execute wrapper functions and associated packages
RUN mkdir -p /root/wfms/trunk/scheduler/scheduler
COPY scheduler/d_haul /root/wfms/trunk/scheduler/
COPY scheduler/dragen_job_execute /root/wfms/trunk/scheduler/
COPY scheduler/scheduler/aws_utils.py /root/wfms/trunk/scheduler/scheduler/
COPY scheduler/scheduler/constants.py /root/wfms/trunk/scheduler/scheduler/
COPY scheduler/scheduler/job_utils.py /root/wfms/trunk/scheduler/scheduler/
COPY scheduler/scheduler/logger.py /root/wfms/trunk/scheduler/scheduler/
COPY scheduler/scheduler/scheduler_utils.py  /root/wfms/trunk/scheduler/scheduler/
COPY scheduler/scheduler/webapi.py /root/wfms/trunk/scheduler/scheduler/
COPY scheduler/scheduler/wfms_exception.py /root/wfms/trunk/scheduler/scheduler/
RUN touch /root/wfms/trunk/scheduler/scheduler/__init__.py
# Landing directory should be where DJX is located
WORKDIR "/root/wfms/trunk/scheduler/"
# Debug print of container's directories
RUN tree /root/wfms/trunk/scheduler
# Default behaviour. Over-ride with --entrypoint on docker run cmd line
ENTRYPOINT ["/root/wfms/trunk/scheduler/dragen_job_execute"]
CMD []

Note: Edico Genome’s custom Python wrapper functions for its Workflow Management System (WFMS) in the latter part of this Dockerfile should be replaced with functions that are specific to your workflow.

The second method is to install binaries and then use Docker as a lightweight connector between AWS Batch and the AFI. For example, this might be a route you would choose to use if you were provisioning DRAGEN from the AWS Marketplace.

In this case, the Dockerfile would not contain the installation of the binaries to run DRAGEN, but would contain any other packages necessary for job completion. When you run your Docker container, you enable Docker to access the underlying file system.

Connecting to AWS Batch

AWS Batch provisions compute resources and runs your jobs, choosing the right instance types based on your job requirements and scaling down resources as work is completed. AWS Batch users submit a job, based on a template or “job definition” to an AWS Batch job queue.

Job queues are mapped to one or more compute environments that describe the quantity and types of resources that AWS Batch can provision. In this case, Edico created a managed compute environment that was able to launch 1,000 EC2 F1 instances across multiple Availability Zones in us-east-1. As jobs are submitted to a job queue, the service launches the required quantity and types of instances that are needed. As instances become available, AWS Batch then runs each job within appropriately sized Docker containers.

The Edico Genome workflow manager API submits jobs to an AWS Batch job queue. This job queue maps to an AWS Batch managed compute environment containing On-Demand F1 instances. In this section, you can set this up yourself.

To create the compute environment that DRAGEN can use:

aws batch create-compute-environment --cli-input-json file://<path_to_json_file>/F1OnDemand.json

Where your JSON file contains the following code (replace with your own resource IDs):


{
 "computeEnvironmentName": "F1OnDemand",
 "type": "MANAGED",
 "state": "ENABLED",
 "computeResources": {
   "type": "EC2",
   "minvCpus": 0,
   "maxvCpus": 128,
   "desiredvCpus": 0,
   "instanceTypes": [
     "f1.2xlarge",
     "f1.16xlarge"
   ],
   "subnets": [
     "subnet-220c0e0a",
     "subnet-1a95556d",
     "subnet-978f6dce"
   ],
   "securityGroupIds": [
     "sg-cf5093b2"
   ],
   "ec2KeyPair": "id_rsa",
   "instanceRole": "ecsInstanceRole",
   "tags": {
     "Name": "Batch Instance – F1OnDemand"
   }
  },
 "serviceRole": "arn:aws:iam::012345678910:role/service-role/AWSBatchServiceRole"
}

And the corresponding job queue:

aws batch create-job-queue --cli-input-json file://<path_to_json_file>/dragen.json

Where dragen.json is as follows:


{
 "jobQueueName": "DRAGEN-OnDemand",
 "state": "ENABLED",
 "priority": 100,
 "computeEnvironmentOrder": 
 [
  {
    "order": 1,
    "computeEnvironment": "F1OnDemand"
  }
 ]
}

An f1.2xlarge EC2 instance contains one FPGA, eight vCPUs, and 122-GiB RAM. As DRAGEN requires an entire FPGA to run, Edico Genome needed to ensure that only one analysis per time executed on an instance. By using the f1.2xlarge vCPUs and memory as a proxy in their AWS Batch job definition, Edico Genome could ensure that only one job runs on an instance at a time. Here’s what that looks like in the AWS CLI:

aws batch register-job-definition --job-definition-name dragen-wgs --type container --container-properties '{ "image": ${DRAGEN_IMAGE}, "vcpus": 8, "memory": 120000}'

Now, you can submit jobs easily to your DRAGEN environment:

aws batch submit-job --job-name dragen-run1 --job-queue DRAGEN-OnDemand --job-definition dragen-wgs --container-overrides command=${RUN_PARAMETERS}

You can query the status of your DRAGEN job with the following command:

aws batch describe-jobs --jobs <the job ID from the above command>

The logs for your job are written to the /aws/batch/job CloudWatch log group.

Conclusion

In this post, we demonstrated how to set up an environment with AWS Batch that can run DRAGEN on EC2 F1 instances at scale. If you followed the walkthrough, you’ve replicated much of the architecture Edico Genome used to set the Guinness World Record.

There are several ways in which you can harness the computational power of DRAGEN to analyze genomes at scale. First, DRAGEN is available through several different genomics platforms, such as the DNAnexus Platform. DRAGEN is also available on the AWS Marketplace. You can apply the architecture presented in this post to build a scalable solution that is both performant and cost-optimized.

For more information about how AWS Batch can facilitate genomics processing at scale, be sure to check out our aws-batch-genomics GitHub repo on high-throughput genomics on AWS.

Hot Startups on AWS – October 2017

Post Syndicated from Tina Barr original https://aws.amazon.com/blogs/aws/hot-startups-on-aws-october-2017/

In 2015, the Centers for Medicare and Medicaid Services (CMS) reported that healthcare spending made up 17.8% of the U.S. GDP – that’s almost $3.2 trillion or $9,990 per person. By 2025, the CMS estimates this number will increase to nearly 20%. As cloud technology evolves in the healthcare and life science industries, we are seeing how companies of all sizes are using AWS to provide powerful and innovative solutions to customers across the globe. This month we are excited to feature the following startups:

  • ClearCare – helping home care agencies operate efficiently and grow their business.
  • DNAnexus – providing a cloud-based global network for sharing and managing genomic data.

ClearCare (San Francisco, CA)

ClearCare envisions a future where home care is the only choice for aging in place. Home care agencies play a critical role in the economy and their communities by significantly lowering the overall cost of care, reducing the number of hospital admissions, and bending the cost curve of aging. Patients receiving home care typically have multiple chronic conditions and functional limitations, driving over $190 billion in healthcare spending in the U.S. each year. To offset these costs, health insurance payers are developing in-home care management programs for patients. ClearCare’s goal is to help home care agencies leverage technology to improve costs, outcomes, and quality of life for the aging population. The company’s powerful software platform is specifically designed for use by non-medical, in-home care agencies to manage their businesses.

Founder and CEO Geoff Nudd created ClearCare because of his own grandmother’s need for care. Keeping family members and caregivers up to date on a loved one’s well being can be difficult, so Geoff created what is now ClearCare’s Family Room, which enables caregivers and agency staff to check schedules and receive real-time updates about what’s happening in the home. Since then, agencies have provided feedback on others areas of their businesses that could be streamlined. ClearCare has now built over 20 modules to help home care agencies optimize operations with services including a telephony service, billing and payroll, and more. ClearCare now serves over 4,000 home care agencies, representing 500,000 caregivers and 400,000 seniors.

Using AWS, ClearCare is able to spin up reliable infrastructure for proofs of concept and iterate on those systems to quickly get value to market. The company runs many AWS services including Amazon Elasticsearch Service, Amazon RDS, and Amazon CloudFront. Amazon EMR and Amazon Athena have enabled ClearCare to build a Hadoop-based ETL and data warehousing system that processes terabytes of data each day. By utilizing these managed services, ClearCare has been able to go from concept to customer delivery in less than three months.

To learn more about ClearCare, check out their website.

DNAnexus (Mountain View, CA)

DNAnexus is accelerating the application of genomic data in precision medicine by providing a cloud-based platform for sharing and managing genomic and biomedical data and analysis tools. The company was founded in 2009 by Stanford graduate student Andreas Sundquist and two Stanford professors Arend Sidow and Serafim Batzoglou, to address the need for scaling secondary analysis of next-generation sequencing (NGS) data in the cloud. The founders quickly learned that users needed a flexible solution to build complex analysis workflows and tools that enable them to share and manage large volumes of data. DNAnexus is optimized to address the challenges of security, scalability, and collaboration for organizations that are pursuing genomic-based approaches to health, both in clinics and research labs. DNAnexus has a global customer base – spanning North America, Europe, Asia-Pacific, South America, and Africa – that runs a million jobs each month and is doubling their storage year-over-year. The company currently stores more than 10 petabytes of biomedical and genomic data. That is equivalent to approximately 100,000 genomes, or in simpler terms, over 50 billion Facebook photos!

DNAnexus is working with its customers to help expand their translational informatics research, which includes expanding into clinical trial genomic services. This will help companies developing different medicines to better stratify clinical trial populations and develop companion tests that enable the right patient to get the right medicine. In collaboration with Janssen Human Microbiome Institute, DNAnexus is also launching Mosaic – a community platform for microbiome research.

AWS provides DNAnexus and its customers the flexibility to grow and scale research programs. Building the technology infrastructure required to manage these projects in-house is expensive and time-consuming. DNAnexus removes that barrier for labs of any size by using AWS scalable cloud resources. The company deploys its customers’ genomic pipelines on Amazon EC2, using Amazon S3 for high-performance, high-durability storage, and Amazon Glacier for low-cost data archiving. DNAnexus is also an AWS Life Sciences Competency Partner.

Learn more about DNAnexus here.

—

-Tina

Building a Real World Evidence Platform on AWS

Post Syndicated from Aaron Friedman original https://aws.amazon.com/blogs/big-data/building-a-real-world-evidence-platform-on-aws/

Deriving insights from large datasets is central to nearly every industry, and life sciences is no exception. To combat the rising cost of bringing drugs to market, pharmaceutical companies are looking for ways to optimize their drug development processes. They are turning to big data analytics to better quantify the effect that their drug compounds have on different populations and to look for new clinical indications for existing drugs.

A real world evidence (RWE) platform is loosely defined as an integrated set of services and products that life sciences companies use to securely acquire, store, and analyze large, often disparate datasets to gain insight into the functions of a specific drug or intervention. The petabyte-scale data generated from wearables, medical devices, genomics, clinical imaging, and claims (to name a few) allows pharmaceutical and other life sciences companies to build big data platforms to analyze these datasets. And many of them are doing it on AWS.

At the center of nearly every RWE platform is a data lake that houses different data types. It also stores the related metadata to identify where each piece of data came from, who owned it, etc. Analytics engines integrate the relevant streaming (e.g., wearables), structured (e.g., claims data), and unstructured (e.g., notes in electronic health records) data.

In this post, I highlight common architectural patterns that customers are using to maximize the value of real world evidence on AWS. The architecture presented here can be reproduced in multiple regions, so you can respect local data sovereignty requirements, when applicable, while conducting global studies. This post doesn’t cover all considerations for real world evidence (such as security and authentication), but instead focuses on the areas that are related to your data flow.

A data lake is your source of truth

The ability to integrate disparate data types is critical to maximizing the utility of RWE. Life sciences companies have to be able to store, search, and retrieve data of different types and sizes, including (but not limited to) the following:

  • Streaming data from wearables and medical devices
  • Structured data from genomics and claims data
  • Unstructured data from notes in electronic health records

A common solution to integrate these data types is a data lake. Data lakes allow organizations to store all their data, regardless of data type, in a centralized repository. Because data can be stored as-is, there’s no need to convert it to a predefined schema. And you no longer need to know what questions you want to ask of your data beforehand. You can use data lakes for ad hoc analyses, so you can quickly explore and discover new insights without needing to structure the data first, as you would with a traditional data warehouse. 

Although there are many uses for storing real world evidence data in a data lake, here are a few examples of the processes it can facilitate for pharma and biotech companies:

  • Quickly access all data for a given subject, from clinical images to genomics to claims data.
  • Associate incoming RWE data with existing data in your RWE data lake, such as by subject or study.
  • Select specific windows in longitudinal studies (microbiome, metabolomics, etc.). 

The following diagram shows an architecture of the features within a data lake and several examples of how data enters a data lake:

This architecture, which is entirely serverless and backed by Amazon S3, lets you scale your data lake to easily accommodate any data size for your RWE platform. Additionally, the components presented in this diagram can be secured via IAM controls and service policies. This enables you to secure and protect the sensitive information that often resides within an RWE platform. Amazon S3 is the canonical source for objects in your RWE data lake. You track and search metadata that is associated with these objects in a data catalog built on Amazon Elasticsearch Service and Amazon DynamoDB.

Let’s dive deeper into what this architecture does and how data makes it into the data lake:

  1. Streaming (e.g., wearables), structured, and unstructured data is acquired from myriad devices and sources. Depending on the data size, you might use AWS IoT (streaming), AWS Storage Gateway (mid-size/continual batch), and AWS Snowball (large legacy datasets, such as imaging). AWS IoT writes to Amazon Kinesis Firehose, which transforms the telemetry data in-flight to land both transformed and raw data in Amazon S3.
  2. When data lands in Amazon S3 buckets, an AWS Lambda function is invoked (either by trigger or manually).
  3. This Lambda function writes to a data catalog that is fronted by Amazon API Gateway. The data catalog contains metadata about all the object data in Amazon S3, as well as data that resides in databases, such as Amazon Redshift.
  4. An AWS Lambda function on the other side of API Gateway writes the appropriate metadata about the objects, such as the study that the data was generated from, into Amazon Elasticsearch Service and/or Amazon DynamoDB, which I refer to as the data catalog. 

The data catalog mentioned in steps 3 and 4 is central to your data management. In most analyses, the first step is to build a data manifest of where the data-of-interest lies, which is discussed in later sections.

Normalizing data for real world evidence

As I previously mentioned, data can enter your real world evidence data lake in many different formats. In many cases, you might need to normalize this data into a specific format to ease downstream analysis. For example, you might have to integrate many different electronic health records that are stored in different formats. You also might have to transform genomics data, often represented as a variant call format (VCF) file, into a format that’s easier for big data technologies like Apache Parquet to query. While you can certainly analyze this data in the format in which it entered, it might be better for querying after it’s transformed into a different format, or into a different data store.

In the architecture shown in the following diagram, AWS Step Functions orchestrates the data normalization (extract, transform, load—or ETL) process. Step Functions is a serverless workflow service that ensures that your long-running ETL jobs execute in order and complete successfully. At a high level, you first query the data catalog to get a manifest of the data to be normalized. Normalization occurs on Amazon EC2 instances, and the results are then stored back in Amazon S3 or in databases such as Amazon Redshift for future analysis. Locations of these results data are also logged in the data catalog for future querying.

Here is an example Step Functions state machine for this process:

The following is the accompanying JSON that produces it:

{
  "Comment": "An example for data normalization in RWE",
  "StartAt": "GenerateDataManifest",
  "States": {
    "GenerateDataManifest": {
      "Type": "Task",
      "Resource": "arn:aws:lambda:us-east-1:<account-id>::function:GenerateDataManifest",
      "Retry": [ {
        "ErrorEquals": ["States.Timeout"],
        "MaxAttempts": 2
      } ],
      "Next": "SubmitNormalizationJob"
    },
    "SubmitNormalizationJob": {
      "Type": "Task",
      "Resource": "arn:aws:lambda:us-east-1:<account-id>::function: SubmitNormalizationJob",
      "Next": "GetNormalizationJobStatus"
    },
    "GetNormalizationJobStatus": {
      "Type": "Task",
      "Resource": "arn:aws:lambda:us-east-1:<account-id>:function: GetNormalizationJobStatus",
      "Next": "CheckJobStatus",
      "InputPath": "$",
      "ResultPath": "$.status"
    },
    "CheckJobStatus": {
      "Type": "Choice",
      "Choices": [
        {
          "Variable": "$.status",
          "StringEquals": "FAILED",
          "Next": "JobFailureCleanup"
        },
        {
          "Variable": "$.status",
          "StringEquals": "SUCCEEDED",
          "Next": "JobSuccessCleanup"
        }
      ],
      "Default": "Wait60Seconds"
    },
    "Wait60Seconds": {
      "Type": "Wait",
      "Seconds": 60,
      "Next": "GetNormalizationJobStatus"
    },
    "JobSuccessCleanup": {
      "Type": "Task",
      "Resource": "arn:aws:lambda:us-east-1:<account-id>:function:batchJobSuccessCleanup",
      "End": true
    },
    "JobFailureCleanup": {
      "Type": "Task",
      "Resource": "arn:aws:lambda:us-east-1:<account-id>:function:batchJobSuccessCleanup",
      "End": true
    }
  }
}

And here is the overall architecture:

Here are the steps in more detail:

  1. Either a user or an application submits a POST request through Amazon API Gateway to process or transform a set of data. This request contains the query parameters that correspond to generating the data manifest. It is passed into an AWS Step Functions state machine, which orchestrates the ETL process.
  2. A Lambda function queries the data catalog and builds a manifest that contains the location of the data of interest.
  3. The manifest is passed to downstream Lambda functions in the state machine that orchestrate the batch workflow to process the data that’s specified in the manifest.
  4. These Lambda functions submit jobs to AWS Batch to execute batch jobs on Amazon EC2.
  5. Amazon EC2 processes the data (e.g., data normalization) and stores results back in Amazon S3 and/or Amazon Redshift.
  6. Results data is then logged in the data catalog, using the process shown in the data acquisition diagram.

Analyzing and visualizing data in real world evidence

Ultimately, the value in real world evidence platforms is the value you derive from the wide variety of data that feeds into it. Big data analytics, such as Spark on EMR, machine learning with the Deep Learning AMIs, and healthcare data warehouses, are all fundamental to maximizing the value of RWE.

Given the wide array of questions you can answer, you want your architecture to be as flexible as possible. Your organization also might use business intelligence (BI) tools. Again, as with the data normalization tier, you can use Step Functions to orchestrate your workflow. You first build the data manifest and then submit each portion of your desired analysis to the relevant data location and compute options, such as Amazon EMR or Amazon Redshift. Your organization’s BI tool of choice, such as Amazon QuickSight or one offered by our AWS Big Data Competency partners, can extract and visualize the results.

Here is the workflow in more detail:

  1. A user initiates a query on a dataset. In the context of RWE, this is largely analyzing a cohort in the context of a specific indication (drug response, etc.).
  2. AWS Step Functions invokes a Lambda function to query the data catalog and build a manifest of data.
  3. The manifest is passed to subsequent Lambda functions, which orchestrate the data analysis through different AWS services.
  4. These analyses can include Amazon EMR for population-scale genomics, Amazon EC2 for HPC and machine learning, and Amazon Redshift for your healthcare data warehouse.
  5. Results from each analysis are staged back in Amazon S3 and logged in the data catalog.
  6. BI tools like Amazon QuickSight or Tableau, or data analysis workbooks like Jupyter can query Amazon S3 to visualize results, such as through Amazon Athena.

A practical example

Imagine that you are at a pharmaceutical company that is developing a drug to address a neurological condition. You have longitudinal data in the form of brain scans and have noticed that the drug compound under development seems to benefit a specific subpopulation of individuals. As a result, you determine to sequence the genomes of all the responders and non-responders to look for specific biomarkers that could indicate response levels. Success would result in a companion diagnostic that you could use alongside your drug to increase its overall efficacy by delivering it only to the responding population.

Let’s briefly look at how this study might play out in the steps of data acquisition, normalization, and analysis described earlier.

Data acquisition

Data from longitudinal brain imaging studies can be moved to AWS using AWS Snowball. Genomics data coming off your genome sequencers would land in Amazon S3 via AWS Storage Gateway and/or AWS Direct Connect. These datasets are stored in your data catalog where you track items such as date generated, anonymized subject ID, etc.

Data processing

Genomes are transformed from raw reads (e.g., FASTQ) to a human readable format (VCF) that identifies variations in a genome. These VCF files are subsequently extracted, transformed, and loaded into a format that is amenable to big data analytics, such as Parquet.

Data analysis

You build a data manifest of your brain scans in Amazon S3. You use the deep learning AMI and P2 instance family to build machine learning models to identify images that represent different stages in your disorder-of-interest. You then run association analyses that combine your genomics data with your brain imaging models and drug response data to identify what genomic variants associate with improved treatment outcomes. You can manage these analyses via Jupyter notebooks, or you can connect with your BI tool of choice to visualize your results.

It will always be day one for RWE

By implementing real world evidence platforms on AWS, you can quickly integrate and interrogate disparate healthcare data to advance human health. By designing your RWE platform to be flexible, you can readily incorporate new big data technologies as they become relevant to your needs. This enables you to innovate and discover at a quicker rate.

To read more about real world evidence on AWS, and how AWS Partner Network (APN) Premier Consulting Partner Deloitte has architected their ConvergeHEALTH platform on AWS, check out this guest post on the APN Blog!

Please leave any questions and comments below.

Additional Reading

The healthcare ecosystem has chosen a variety of tools and techniques for working with big data, but one tool that comes up again and again in many of the architectures we design and review is Spark on Amazon EMR. Will spark power the data behind precision medicine?

About the Authors

Dr. Aaron Friedman is a Healthcare and Life Sciences Partner Solutions Architect at Amazon Web Services. He works with ISVs and SIs to architect healthcare solutions on AWS, and bring the best possible experience to their customers. His passion is working at the intersection of science, big data, and software. In his spare time, he’s exploring the outdoors, learning a new thing to cook, or spending time with his wife and his dog, Macaroon.

 

 

 

Analyze OpenFDA Data in R with Amazon S3 and Amazon Athena

Post Syndicated from Ryan Hood original https://aws.amazon.com/blogs/big-data/analyze-openfda-data-in-r-with-amazon-s3-and-amazon-athena/

One of the great benefits of Amazon S3 is the ability to host, share, or consume public data sets. This provides transparency into data to which an external data scientist or developer might not normally have access. By exposing the data to the public, you can glean many insights that would have been difficult with a data silo.

The openFDA project creates easy access to the high value, high priority, and public access data of the Food and Drug Administration (FDA). The data has been formatted and documented in consumer-friendly standards. Critical data related to drugs, devices, and food has been harmonized and can easily be called by application developers and researchers via API calls. OpenFDA has published two whitepapers that drill into the technical underpinnings of the API infrastructure as well as how to properly analyze the data in R. In addition, FDA makes openFDA data available on S3 in raw format.

In this post, I show how to use S3, Amazon EMR, and Amazon Athena to analyze the drug adverse events dataset. A drug adverse event is an undesirable experience associated with the use of a drug, including serious drug side effects, product use errors, product quality programs, and therapeutic failures.

Data considerations

Keep in mind that this data does have limitations. In addition, in the United States, these adverse events are submitted to the FDA voluntarily from consumers so there may not be reports for all events that occurred. There is no certainty that the reported event was actually due to the product. The FDA does not require that a causal relationship between a product and event be proven, and reports do not always contain the detail necessary to evaluate an event. Because of this, there is no way to identify the true number of events. The important takeaway to all this is that the information contained in this data has not been verified to produce cause and effect relationships. Despite this disclaimer, many interesting insights and value can be derived from the data to accelerate drug safety research.

Data analysis using SQL

For application developers who want to perform targeted searching and lookups, the API endpoints provided by the openFDA project are “ready to go” for software integration using a standard API powered by Elasticsearch, NodeJS, and Docker. However, for data analysis purposes, it is often easier to work with the data using SQL and statistical packages that expect a SQL table structure. For large-scale analysis, APIs often have query limits, such as 5000 records per query. This can cause extra work for data scientists who want to analyze the full dataset instead of small subsets of data.

To address the concern of requiring all the data in a single dataset, the openFDA project released the full 100 GB of harmonized data files that back the openFDA project onto S3. Athena is an interactive query service that makes it easy to analyze data in S3 using standard SQL. It’s a quick and easy way to answer your questions about adverse events and aspirin that does not require you to spin up databases or servers.

While you could point tools directly at the openFDA S3 files, you can find greatly improved performance and use of the data by following some of the preparation steps later in this post.

Architecture

This post explains how to use the following architecture to take the raw data provided by openFDA, leverage several AWS services, and derive meaning from the underlying data.

Steps:

  1. Load the openFDA /drug/event dataset into Spark and convert it to gzip to allow for streaming.
  2. Transform the data in Spark and save the results as a Parquet file in S3.
  3. Query the S3 Parquet file with Athena.
  4. Perform visualization and analysis of the data in R and Python on Amazon EC2.

Optimizing public data sets: A primer on data preparation

Those who want to jump right into preparing the files for Athena may want to skip ahead to the next section.

Transforming, or pre-processing, files is a common task for using many public data sets. Before you jump into the specific steps for transforming the openFDA data files into a format optimized for Athena, I thought it would be worthwhile to provide a quick exploration on the problem.

Making a dataset in S3 efficiently accessible with minimal transformation for the end user has two key elements:

  1. Partitioning the data into objects that contain a complete part of the data (such as data created within a specific month).
  2. Using file formats that make it easy for applications to locate subsets of data (for example, gzip, Parquet, ORC, etc.).

With these two key elements in mind, you can now apply transformations to the openFDA adverse event data to prepare it for Athena. You might find the data techniques employed in this post to be applicable to many of the questions you might want to ask of the public data sets stored in Amazon S3.

Before you get started, I encourage those who are interested in doing deeper healthcare analysis on AWS to make sure that you first read the AWS HIPAA Compliance whitepaper. This covers the information necessary for processing and storing patient health information (PHI).

Also, the adverse event analysis shown for aspirin is strictly for demonstration purposes and should not be used for any real decision or taken as anything other than a demonstration of AWS capabilities. However, there have been robust case studies published that have explored a causal relationship between aspirin and adverse reactions using OpenFDA data. If you are seeking research on aspirin or its risks, visit organizations such as the Centers for Disease Control and Prevention (CDC) or the Institute of Medicine (IOM).

Preparing data for Athena

For this walkthrough, you will start with the FDA adverse events dataset, which is stored as JSON files within zip archives on S3. You then convert it to Parquet for analysis. Why do you need to convert it? The original data download is stored in objects that are partitioned by quarter.

Here is a small sample of what you find in the adverse events (/drugs/event) section of the openFDA website.

If you were looking for events that happened in a specific quarter, this is not a bad solution. For most other scenarios, such as looking across the full history of aspirin events, it requires you to access a lot of data that you won’t need. The zip file format is not ideal for using data in place because zip readers must have random access to the file, which means the data can’t be streamed. Additionally, the zip files contain large JSON objects.

To read the data in these JSON files, a streaming JSON decoder must be used or a computer with a significant amount of RAM must decode the JSON. Opening up these files for public consumption is a great start. However, you still prepare the data with a few lines of Spark code so that the JSON can be streamed.

Step 1:  Convert the file types

Using Apache Spark on EMR, you can extract all of the zip files and pull out the events from the JSON files. To do this, use the Scala code below to deflate the zip file and create a text file. In addition, compress the JSON files with gzip to improve Spark’s performance and reduce your overall storage footprint. The Scala code can be run in either the Spark Shell or in an Apache Zeppelin notebook on your EMR cluster.

If you are unfamiliar with either Apache Zeppelin or the Spark Shell, the following posts serve as great references:

 

import scala.io.Source
import java.util.zip.ZipInputStream
import org.apache.spark.input.PortableDataStream
import org.apache.hadoop.io.compress.GzipCodec

// Input Directory
val inputFile = "s3://download.open.fda.gov/drug/event/2015q4/*.json.zip";

// Output Directory
val outputDir = "s3://{YOUR OUTPUT BUCKET HERE}/output/2015q4/";

// Extract zip files from 
val zipFiles = sc.binaryFiles(inputFile);

// Process zip file to extract the json as text file and save it
// in the output directory 
val rdd = zipFiles.flatMap((file: (String, PortableDataStream)) => {
    val zipStream = new ZipInputStream(file.2.open)
    val entry = zipStream.getNextEntry
    val iter = Source.fromInputStream(zipStream).getLines
    iter
}).map(.replaceAll("\s+","")).saveAsTextFile(outputDir, classOf[GzipCodec])

Step 2:  Transform JSON into Parquet

With just a few more lines of Scala code, you can use Spark’s abstractions to convert the JSON into a Spark DataFrame and then export the data back to S3 in Parquet format.

Spark requires the JSON to be in JSON Lines format to be parsed correctly into a DataFrame.

// Output Parquet directory
val outputDir = "s3://{YOUR OUTPUT BUCKET NAME}/output/drugevents"
// Input json file
val inputJson = "s3://{YOUR OUTPUT BUCKET NAME}/output/2015q4/*”
// Load dataframe from json file multiline 
val df = spark.read.json(sc.wholeTextFiles(inputJson).values)
// Extract results from dataframe
val results = df.select("results")
// Save it to Parquet
results.write.parquet(outputDir)

Step 3:  Create an Athena table

With the data cleanly prepared and stored in S3 using the Parquet format, you can now place an Athena table on top of it to get a better understanding of the underlying data.

Because the openFDA data structure incorporates several layers of nesting, it can be a complex process to try to manually derive the underlying schema in a Hive-compatible format. To shorten this process, you can load the top row of the DataFrame from the previous step into a Hive table within Zeppelin and then extract the “create  table” statement from SparkSQL.

results.createOrReplaceTempView("data")

val top1 = spark.sql("select * from data tablesample(1 rows)")

top1.write.format("parquet").mode("overwrite").saveAsTable("drugevents")

val show_cmd = spark.sql("show create table drugevents”).show(1, false)

This returns a “create table” statement that you can almost paste directly into the Athena console. Make some small modifications (adding the word “external” and replacing “using with “stored as”), and then execute the code in the Athena query editor. The table is created.

For the openFDA data, the DDL returns all string fields, as the date format used in your dataset does not conform to the yyy-mm-dd hh:mm:ss[.f…] format required by Hive. For your analysis, the string format works appropriately but it would be possible to extend this code to use a Presto function to convert the strings into time stamps.

CREATE EXTERNAL TABLE  drugevents (
   companynumb  string, 
   safetyreportid  string, 
   safetyreportversion  string, 
   receiptdate  string, 
   patientagegroup  string, 
   patientdeathdate  string, 
   patientsex  string, 
   patientweight  string, 
   serious  string, 
   seriousnesscongenitalanomali  string, 
   seriousnessdeath  string, 
   seriousnessdisabling  string, 
   seriousnesshospitalization  string, 
   seriousnesslifethreatening  string, 
   seriousnessother  string, 
   actiondrug  string, 
   activesubstancename  string, 
   drugadditional  string, 
   drugadministrationroute  string, 
   drugcharacterization  string, 
   drugindication  string, 
   drugauthorizationnumb  string, 
   medicinalproduct  string, 
   drugdosageform  string, 
   drugdosagetext  string, 
   reactionoutcome  string, 
   reactionmeddrapt  string, 
   reactionmeddraversionpt  string)
STORED AS parquet
LOCATION
  's3://{YOUR TARGET BUCKET}/output/drugevents'

With the Athena table in place, you can start to explore the data by running ad hoc queries within Athena or doing more advanced statistical analysis in R.

Using SQL and R to analyze adverse events

Using the openFDA data with Athena makes it very easy to translate your questions into SQL code and perform quick analysis on the data. After you have prepared the data for Athena, you can begin to explore the relationship between aspirin and adverse drug events, as an example. One of the most common metrics to measure adverse drug events is the Proportional Reporting Ratio (PRR). It is defined as:

PRR = (m/n)/( (M-m)/(N-n) )
Where
m = #reports with drug and event
n = #reports with drug
M = #reports with event in database
N = #reports in database

Gastrointestinal haemorrhage has the highest PRR of any reaction to aspirin when viewed in aggregate. One question you may want to ask is how the PRR has trended on a yearly basis for gastrointestinal haemorrhage since 2005.

Using the following query in Athena, you can see the PRR trend of “GASTROINTESTINAL HAEMORRHAGE” reactions with “ASPIRIN” since 2005:

with drug_and_event as 
(select rpad(receiptdate, 4, 'NA') as receipt_year
    , reactionmeddrapt
    , count(distinct (concat(safetyreportid,receiptdate,reactionmeddrapt))) as reports_with_drug_and_event 
from fda.drugevents
where rpad(receiptdate,4,'NA') 
     between '2005' and '2015' 
     and medicinalproduct = 'ASPIRIN'
     and reactionmeddrapt= 'GASTROINTESTINAL HAEMORRHAGE'
group by reactionmeddrapt, rpad(receiptdate, 4, 'NA') 
), reports_with_drug as 
(
select rpad(receiptdate, 4, 'NA') as receipt_year
    , count(distinct (concat(safetyreportid,receiptdate,reactionmeddrapt))) as reports_with_drug 
 from fda.drugevents 
 where rpad(receiptdate,4,'NA') 
     between '2005' and '2015' 
     and medicinalproduct = 'ASPIRIN'
group by rpad(receiptdate, 4, 'NA') 
), reports_with_event as 
(
   select rpad(receiptdate, 4, 'NA') as receipt_year
    , count(distinct (concat(safetyreportid,receiptdate,reactionmeddrapt))) as reports_with_event 
   from fda.drugevents
   where rpad(receiptdate,4,'NA') 
     between '2005' and '2015' 
     and reactionmeddrapt= 'GASTROINTESTINAL HAEMORRHAGE'
   group by rpad(receiptdate, 4, 'NA')
), total_reports as 
(
   select rpad(receiptdate, 4, 'NA') as receipt_year
    , count(distinct (concat(safetyreportid,receiptdate,reactionmeddrapt))) as total_reports 
   from fda.drugevents
   where rpad(receiptdate,4,'NA') 
     between '2005' and '2015' 
   group by rpad(receiptdate, 4, 'NA')
)
select  drug_and_event.receipt_year, 
(1.0 * drug_and_event.reports_with_drug_and_event/reports_with_drug.reports_with_drug)/ (1.0 * (reports_with_event.reports_with_event- drug_and_event.reports_with_drug_and_event)/(total_reports.total_reports-reports_with_drug.reports_with_drug)) as prr
, drug_and_event.reports_with_drug_and_event
, reports_with_drug.reports_with_drug
, reports_with_event.reports_with_event
, total_reports.total_reports
from drug_and_event
    inner join reports_with_drug on  drug_and_event.receipt_year = reports_with_drug.receipt_year   
    inner join reports_with_event on  drug_and_event.receipt_year = reports_with_event.receipt_year
    inner join total_reports on  drug_and_event.receipt_year = total_reports.receipt_year
order by  drug_and_event.receipt_year

One nice feature of Athena is that you can quickly connect to it via R or any other tool that can use a JDBC driver to visualize the data and understand it more clearly.

With this quick R script that can be run in R Studio either locally or on an EC2 instance, you can create a visualization of the PRR and Reporting Odds Ratio (RoR) for “GASTROINTESTINAL HAEMORRHAGE” reactions from “ASPIRIN” since 2005 to better understand these trends.

# connect to ATHENA
conn <- dbConnect(drv, '<Your JDBC URL>',s3_staging_dir="<Your S3 Location>",user=Sys.getenv(c("USER_NAME"),password=Sys.getenv(c("USER_PASSWORD"))

# Declare Adverse Event
adverseEvent <- "'GASTROINTESTINAL HAEMORRHAGE'"

# Build SQL Blocks
sqlFirst <- "SELECT rpad(receiptdate, 4, 'NA') as receipt_year, count(DISTINCT safetyreportid) as event_count FROM fda.drugsflat WHERE rpad(receiptdate,4,'NA') between '2005' and '2015'"
sqlEnd <- "GROUP BY rpad(receiptdate, 4, 'NA') ORDER BY receipt_year"

# Extract Aspirin with adverse event counts
sql <- paste(sqlFirst,"AND medicinalproduct ='ASPIRIN' AND reactionmeddrapt=",adverseEvent, sqlEnd,sep=" ")
aspirinAdverseCount = dbGetQuery(conn,sql)

# Extract Aspirin counts
sql <- paste(sqlFirst,"AND medicinalproduct ='ASPIRIN'", sqlEnd,sep=" ")
aspirinCount = dbGetQuery(conn,sql)

# Extract adverse event counts
sql <- paste(sqlFirst,"AND reactionmeddrapt=",adverseEvent, sqlEnd,sep=" ")
adverseCount = dbGetQuery(conn,sql)

# All Drug Adverse event Counts
sql <- paste(sqlFirst, sqlEnd,sep=" ")
allDrugCount = dbGetQuery(conn,sql)

# Select correct rows
selAll =  allDrugCount$receipt_year == aspirinAdverseCount$receipt_year
selAspirin = aspirinCount$receipt_year == aspirinAdverseCount$receipt_year
selAdverse = adverseCount$receipt_year == aspirinAdverseCount$receipt_year

# Calculate Numbers
m <- c(aspirinAdverseCount$event_count)
n <- c(aspirinCount[selAspirin,2])
M <- c(adverseCount[selAdverse,2])
N <- c(allDrugCount[selAll,2])

# Calculate proptional reporting ratio
PRR = (m/n)/((M-m)/(N-n))

# Calculate reporting Odds Ratio
d = n-m
D = N-M
ROR = (m/d)/(M/D)

# Plot the PRR and ROR
g_range <- range(0, PRR,ROR)
g_range[2] <- g_range[2] + 3
yearLen = length(aspirinAdverseCount$receipt_year)
axis(1,1:yearLen,lab=ax)
plot(PRR, type="o", col="blue", ylim=g_range,axes=FALSE, ann=FALSE)
axis(1,1:yearLen,lab=ax)
axis(2, las=1, at=1*0:g_range[2])
box()
lines(ROR, type="o", pch=22, lty=2, col="red")

As you can see, the PRR and RoR have both remained fairly steady over this time range. With the R Script above, all you need to do is change the adverseEvent variable from GASTROINTESTINAL HAEMORRHAGE to another type of reaction to analyze and compare those trends.

Summary

In this walkthrough:

  • You used a Scala script on EMR to convert the openFDA zip files to gzip.
  • You then transformed the JSON blobs into flattened Parquet files using Spark on EMR.
  • You created an Athena DDL so that you could query these Parquet files residing in S3.
  • Finally, you pointed the R package at the Athena table to analyze the data without pulling it into a database or creating your own servers.

If you have questions or suggestions, please comment below.

Next Steps

Take your skills to the next level. Learn how to optimize Amazon S3 for an architecture commonly used to enable genomic data analysis. Also, be sure to read more about running R on Amazon Athena.

 

 

 

 

 

About the Authors

Ryan Hood is a Data Engineer for AWS. He works on big data projects leveraging the newest AWS offerings. In his spare time, he enjoys watching the Cubs win the World Series and attempting to Sous-vide anything he can find in his refrigerator.

 

 

Vikram Anand is a Data Engineer for AWS. He works on big data projects leveraging the newest AWS offerings. In his spare time, he enjoys playing soccer and watching the NFL & European Soccer leagues.

 

 

Dave Rocamora is a Solutions Architect at Amazon Web Services on the Open Data team. Dave is based in Seattle and when he is not opening data, he enjoys biking and drinking coffee outside.

 

 

 

 

Healthcare Industry Cybersecurity Report

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2017/06/healthcare_indu.html

New US government report: “Report on Improving Cybersecurity in the Health Care Industry.” It’s pretty scathing, but nothing in it will surprise regular readers of this blog.

It’s worth reading the executive summary, and then skimming the recommendations. Recommendations are in six areas.

The Task Force identified six high-level imperatives by which to organize its recommendations and action items. The imperatives are:

  1. Define and streamline leadership, governance, and expectations for health care industry cybersecurity.
  2. Increase the security and resilience of medical devices and health IT.

  3. Develop the health care workforce capacity necessary to prioritize and ensure cybersecurity awareness and technical capabilities.

  4. Increase health care industry readiness through improved cybersecurity awareness and education.

  5. Identify mechanisms to protect research and development efforts and intellectual property from attacks or exposure.

  6. Improve information sharing of industry threats, weaknesses, and mitigations.

News article.

Slashdot thread.

AWS Enables Consortium Science to Accelerate Discovery

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-enables-consortium-science-to-accelerate-discovery/

My colleague Mia Champion is a scientist (check out her publications), an AWS Certified Solutions Architect, and an AWS Certified Developer. The time that she spent doing research on large-data datasets gave her an appreciation for the value of cloud computing in the bioinformatics space, which she summarizes and explains in the guest post below!

— Jeff;

Technological advances in scientific research continue to enable the collection of exponentially growing datasets that are also increasing in the complexity of their content. The global pace of innovation is now also fueled by the recent cloud-computing revolution, which provides researchers with a seemingly boundless scalable and agile infrastructure. Now, researchers can remove the hindrances of having to own and maintain their own sequencers, microscopes, compute clusters, and more. Using the cloud, scientists can easily store, manage, process and share datasets for millions of patient samples with gigabytes and more of data for each individual. As American physicist, John Bardeen once said: “Science is a collaborative effort. The combined results of several people working together is much more effective than could be that of an individual scientist working alone”.

Prioritizing Reproducible Innovation, Democratization, and Data Protection
Today, we have many individual researchers and organizations leveraging secure cloud enabled data sharing on an unprecedented scale and producing innovative, customized analytical solutions using the AWS cloud.  But, can secure data sharing and analytics be done on such a collaborative scale as to revolutionize the way science is done across a domain of interest or even across discipline/s of science? Can building a cloud-enabled consortium of resources remove the analytical variability that leads to diminished reproducibility, which has long plagued the interpretability and impact of research discoveries? The answers to these questions are ‘yes’ and initiatives such as the Neuro Cloud Consortium, The Global Alliance for Genomics and Health (GA4GH), and The Sage Bionetworks Synapse platform, which powers many research consortiums including the DREAM challenges, are starting to put into practice model cloud-initiatives that will not only provide impactful discoveries in the areas of neuroscience, infectious disease, and cancer, but are also revolutionizing the way in which scientific research is done.

Bringing Crowd Developed Models, Algorithms, and Functions to the Data
Collaborative projects have traditionally allowed investigators to download datasets such as those used for comparative sequence analysis or for training a deep learning algorithm on medical imaging data. Investigators were then able to develop and execute their analysis using institutional clusters, local workstations, or even laptops:

This method of collaboration is problematic for many reasons. The first concern is data security, since dataset download essentially permits “chain-data-sharing” with any number of recipients. Second, analytics done using compute environments that are not templated at some level introduces the risk of variable analytics that itself is not reproducible by a different investigator, or even the same investigator using a different compute environment. Third, the required data dump, processing, and then re-upload or distribution to the collaborative group is highly inefficient and dependent upon each individual’s networking and compute capabilities. Overall, traditional methods of scientific collaboration have introduced methods in which security is compromised and time to discovery is hampered.

Using the AWS cloud, collaborative researchers can share datasets easily and securely by taking advantage of Identity and Access Management (IAM) policy restrictions for user bucket access as well as S3 bucket policies or Access Control Lists (ACLs). To streamline analysis and ensure data security, many researchers are eliminating the necessity to download datasets entirely by leveraging resources that facilitate moving the analytics to the data source and/or taking advantage of remote API requests to access a shared database or data lake. One way our customers are accomplishing this is to leverage container based Docker technology to provide collaborators with a way to submit algorithms or models for execution on the system hosting the shared datasets:

Docker container images have all of the application’s dependencies bundled together, and therefore provide a high degree of versatility and portability, which is a significant advantage over using other executable-based approaches. In the case of collaborative machine learning projects, each docker container will contain applications, language runtime, packages and libraries, as well as any of the more popular deep learning frameworks commonly used by researchers including: MXNet, Caffe, TensorFlow, and Theano.

A common feature in these frameworks is the ability to leverage a host machine’s Graphical Processing Units (GPUs) for significant acceleration of the matrix and vector operations involved in the machine learning computations. As such, researchers with these objectives can leverage EC2’s new P2 instance types in order to power execution of submitted machine learning models. In addition, GPUs can be mounted directly to containers using the NVIDIA Docker tool and appear at the system level as additional devices. By leveraging Amazon EC2 Container Service and the EC2 Container Registry, collaborators are able to execute analytical solutions submitted to the project repository by their colleagues in a reproducible fashion as well as continue to build on their existing environment.  Researchers can also architect a continuous deployment pipeline to run their docker-enabled workflows.

In conclusion, emerging cloud-enabled consortium initiatives serve as models for the broader research community for how cloud-enabled community science can expedite discoveries in Precision Medicine while also providing a platform where data security and discovery reproducibility is inherent to the project execution.

— Mia D. Champion, Ph.D.

 

Pollexy – Building a Special Needs Voice Assistant with Amazon Polly and Raspberry Pi

Post Syndicated from Ana Visneski original https://aws.amazon.com/blogs/aws/pollexy-building-a-special-needs-voice-assistant-with-amazon-polly-and-raspberry-pi/

April is Autism Awareness month and about 1 in 68 children in the U.S. have been identified with autism spectrum disorder (ASD) (CDC 2014). In this post from Troy Larson, a Sr. Devops Cloud Architect here at AWS, you get an introduction to a project he has been working on to help his son Calvin.

I have been asked how the minds at AWS come up with so many different ideas. Sometimes they come from a deeply personal place, where someone sees a way to help others. Pollexy is an amazing example of just that. Read about Pollexy and then watch the video here.

-Ana

Background

As a computer programming parent of a 16-year old non-verbal teenage boy with autism, I have been constantly searching over the years to find ways to use technology to make our lives together safer, happier and more comfortable. At the core of this challenge is the most basic of all human interaction—communication. While Calvin is able to respond to verbal instruction, he is not able to speak responsively. In his entire life, we’ve never had a conversation. He is able to be left alone in his room to play, but most every task or set of tasks requires a human to verbally prompt him along the way. Having other children and responsibilities in the home, at times the intensity of supervision can be negatively impactful on the home dynamic.

Genesis

When I saw the announcement of Amazon Polly and Amazon Lex at re:Invent last year, I immediately started churning on how we could leverage these technologies to assist Calvin. He responds well to human verbal prompts, but would he understand a digital voice? So one Saturday, I setup a Raspberry Pi in his room and closed his door and crouched around the corner with other family members so Calvin couldn’t see us. I connected to the Raspberry Pi and instructed Polly to speak in Joanna’s familiar pacific tone, “Calvin, it’s time to take a potty break. Go out of your bedroom and go to the bathroom.” In a few seconds, we heard his doorknob turn and I poked my head out of my hiding place. Calvin passed by, looking at me quizzically, then went into the bathroom as Joanna had instructed. We all looked at each other in amazement—he had listened and responded perfectly to the completely invisible voice of someone he’d never heard before. After discussing some ideas around this with co-workers, a colleague suggested I enter the IoT and AI Science Fair at our annual AWS Sales Kick-Off meeting. Less than two months after the Polly and Lex announcement and 3500 lines of code later, Pollexy—along with Calvin–debuted at the Science Fair.

Overview

Pollexy (“Polly” + “Lex”) is a Raspberry Pi and mobile-based special needs verbal assistant that lets caretakers schedule audio task prompts and messages both on a recurring schedule and/or on-demand. Caretakers can schedule regular medicine reminder messages or hourly bathroom break messages, for example, and at the same time use their Amazon Echo and mobile device to request a specific message be played immediately. Caretakers can even set it up so that the person needs to confirm that they’ve heard the message. For example, my son won’t pay attention to Pollexy unless Pollexy first asks him to “Push the blue button.” Pollexy will wait until he has pushed the button and then speak the actual message. Other people may be able to respond verbally using Lex, or not require a confirmation at all. Pollexy can be tailored to what works best.

And then most importantly—and most challenging—in a large house, how do we make sure the person is in the room where we play the message? What if we have a special needs adult living in an in-law suite? Are they in the living room or the kitchen? And what about multiple people? What if we have multiple people in different areas of the house, each of whom has a message? Let’s explore the basic elements and tie the pieces together.

Basic Elements of Pollexy

In the spirit of Amazon’s Leadership Principle “Invent and Simplify,” we want to minimize the complexity of the Pollexy architecture. We can break Pollexy down into three types of objects and three components, all of which work together in a way that’s easily explainable.

Object #1: Person

Pollexy can support any number of people. A person is a uniquely identifiable name. We can set basic preferences such as “requires confirmation” and most importantly, we can define a location schedule. This means that we can create an Outlook-like schedule that sets preferences where someone should be in the house.

Object #2: Location

A location is simply a uniquely identifiable location where a device is physically sitting. Based on the user’s location schedule, Pollexy will know which device to contact first, second, third, etc. We can also “mute” devices if needed (naptime, etc.)

Object #3: Message

Obviously, this is the actual message we want to play. Attached to each message is a person and a recurring schedule (only if it’s not a one-time message). We don’t store location with the message, because Pollexy figures out the person’s location when the message is ready to be delivered.

Component #1: Scheduler

Every message needs to be scheduled. This is a command-line tool where you basically say Tell “Calvin” that “you need to brush your teeth” every night at 8 p.m. This message is then stored in DynamoDB, waiting to be picked up by the queueing Lambda function.

Component #2: Queueing Engine

Every minute, a Lambda runs and checks the scheduler to see if there is a message or messages ready to be delivered. If a message is ready, it looks up the person’s location schedule and figures out where they are and then pushes the message or messages into an SQS queue for that location.

Component #3: Speaker Engine

Every minute on the Raspberry Pi device, the speaker engine spins up and checks the SQS for its location. If there are messages, then the speaker engine looks at the user’s preferences and initiates communication to convey the message. If the person doesn’t respond, the speaker engine will check if the person has a secondary location in their schedule and drop the message in the SQS Queue for that location. In the end, a message will either be delivered or eventually just timeout (if someone is out of the house for the day).

Respect and Freedom are the Keys

We often take our personal privacy and respect for granted, so imagine even for a special needs person, the lack of privacy and freedom around having a person constantly in your presence. This is exaggerated for those in the autism spectrum where invasion of personal space can escalate a sense of invasion, turning into anger and frustration. Pollexy becomes their own personal, gentle and never-flustered friend to coach to them along the way, giving them confidence, respect and the sense of privacy and freedom we all want to enjoy.

-Troy Larson

AWS Hot Startups – March 2017

Post Syndicated from Ana Visneski original https://aws.amazon.com/blogs/aws/aws-hot-startups-march-2017/

As the madness of March rounds up, take a break from all the basketball and check out the cool startups Tina Barr brings you for this month!

-Ana

The arrival of spring brings five new startups this month:

  • Amino Apps – providing social networks for hundreds of thousands of communities.
  • Appboy – empowering brands to strengthen customer relationships.
  • Arterys – revolutionizing the medical imaging industry.
  • Protenus – protecting patient data for healthcare organizations.
  • Syapse – improving targeted cancer care with shared data from across the country.

In case you missed them, check out February’s hot startups here.

Amino Apps (New York, NY)
Amino LogoAmino Apps was founded on the belief that interest-based communities were underdeveloped and outdated, particularly when it came to mobile. CEO Ben Anderson and CTO Yin Wang created the app to give users access to hundreds of thousands of communities, each of them a complete social network dedicated to a single topic. Some of the largest communities have over 1 million members and are built around topics like popular TV shows, video games, sports, and an endless number of hobbies and other interests. Amino hosts communities from around the world and is currently available in six languages with many more on the way.

Navigating the Amino app is easy. Simply download the app (iOS or Android), sign up with a valid email address, choose a profile picture, and start exploring. Users can search for communities and join any that fit their interests. Each community has chatrooms, multimedia content, quizzes, and a seamless commenting system. If a community doesn’t exist yet, users can create it in minutes using the Amino Creator and Manager app (ACM). The largest user-generated communities are turned into their own apps, which gives communities their own piece of real estate on members’ phones, as well as in app stores.

Amino’s vast global network of hundreds of thousands of communities is run on AWS services. Every day users generate, share, and engage with an enormous amount of content across hundreds of mobile applications. By leveraging AWS services including Amazon EC2, Amazon RDS, Amazon S3, Amazon SQS, and Amazon CloudFront, Amino can continue to provide new features to their users while scaling their service capacity to keep up with user growth.

Interested in joining Amino? Check out their jobs page here.

Appboy (New York, NY)
In 2011, Bill Magnuson, Jon Hyman, and Mark Ghermezian saw a unique opportunity to strengthen and humanize relationships between brands and their customers through technology. The trio created Appboy to empower brands to build long-term relationships with their customers and today they are the leading lifecycle engagement platform for marketing, growth, and engagement teams. The team recognized that as rapid mobile growth became undeniable, many brands were becoming frustrated with the lack of compelling and seamless cross-channel experiences offered by existing marketing clouds. Many of today’s top mobile apps and enterprise companies trust Appboy to take their marketing to the next level. Appboy manages user profiles for nearly 700 million monthly active users, and is used to power more than 10 billion personalized messages monthly across a multitude of channels and devices.

Appboy creates a holistic user profile that offers a single view of each customer. That user profile in turn powers contextual cross-channel messaging, lifecycle engagement automation, and robust campaign insights and optimization opportunities. Appboy offers solutions that allow brands to create push notifications, targeted emails, in-app and in-browser messages, news feed cards, and webhooks to enhance the user experience and increase customer engagement. The company prides itself on its interoperability, connecting to a variety of complimentary marketing tools and technologies so brands can build the perfect stack to enable their strategies and experiments in real time.

AWS makes it easy for Appboy to dynamically size all of their service components and automatically scale up and down as needed. They use an array of services including Elastic Load Balancing, AWS Lambda, Amazon CloudWatch, Auto Scaling groups, and Amazon S3 to help scale capacity and better deal with unpredictable customer loads.

To keep up with the latest marketing trends and tactics, visit the Appboy digital magazine, Relate. Appboy was also recently featured in the #StartupsOnAir video series where they gave insight into their AWS usage.

Arterys (San Francisco, CA)
Getting test results back from a physician can often be a time consuming and tedious process. Clinicians typically employ a variety of techniques to manually measure medical images and then make their assessments. Arterys founders Fabien Beckers, John Axerio-Cilies, Albert Hsiao, and Shreyas Vasanawala realized that much more computation and advanced analytics were needed to harness all of the valuable information in medical images, especially those generated by MRI and CT scanners. Clinicians were often skipping measurements and making assessments based mostly on qualitative data. Their solution was to start a cloud/AI software company focused on accelerating data-driven medicine with advanced software products for post-processing of medical images.

Arterys’ products provide timely, accurate, and consistent quantification of images, improve speed to results, and improve the quality of the information offered to the treating physician. This allows for much better tracking of a patient’s condition, and thus better decisions about their care. Advanced analytics, such as deep learning and distributed cloud computing, are used to process images. The first Arterys product can contour cardiac anatomy as accurately as experts, but takes only 15-20 seconds instead of the 45-60 minutes required to do it manually. Their computing cloud platform is also fully HIPAA compliant.

Arterys relies on a variety of AWS services to process their medical images. Using deep learning and other advanced analytic tools, Arterys is able to render images without latency over a web browser using AWS G2 instances. They use Amazon EC2 extensively for all of their compute needs, including inference and rendering, and Amazon S3 is used to archive images that aren’t needed immediately, as well as manage costs. Arterys also employs Amazon Route 53, AWS CloudTrail, and Amazon EC2 Container Service.

Check out this quick video about the technology that Arterys is creating. They were also recently featured in the #StartupsOnAir video series and offered a quick demo of their product.

Protenus (Baltimore, MD)
Protenus LogoProtenus founders Nick Culbertson and Robert Lord were medical students at Johns Hopkins Medical School when they saw first-hand how Electronic Health Record (EHR) systems could be used to improve patient care and share clinical data more efficiently. With increased efficiency came a huge issue – an onslaught of serious security and privacy concerns. Over the past two years, 140 million medical records have been breached, meaning that approximately 1 in 3 Americans have had their health data compromised. Health records contain a repository of sensitive information and a breach of that data can cause major havoc in a patient’s life – namely identity theft, prescription fraud, Medicare/Medicaid fraud, and improper performance of medical procedures. Using their experience and knowledge from former careers in the intelligence community and involvement in a leading hedge fund, Nick and Robert developed the prototype and algorithms that launched Protenus.

Today, Protenus offers a number of solutions that detect breaches and misuse of patient data for healthcare organizations nationwide. Using advanced analytics and AI, Protenus’ health data insights platform understands appropriate vs. inappropriate use of patient data in the EHR. It also protects privacy, aids compliance with HIPAA regulations, and ensures trust for patients and providers alike.

Protenus built and operates its SaaS offering atop Amazon EC2, where Dedicated Hosts and encrypted Amazon EBS volume are used to ensure compliance with HIPAA regulation for the storage of Protected Health Information. They use Elastic Load Balancing and Amazon Route 53 for DNS, enabling unique, secure client specific access points to their Protenus instance.

To learn more about threats to patient data, read Hospitals’ Biggest Threat to Patient Data is Hiding in Plain Sight on the Protenus blog. Also be sure to check out their recent video in the #StartupsOnAir series for more insight into their product.

Syapse (Palo Alto, CA)
Syapse provides a comprehensive software solution that enables clinicians to treat patients with precision medicine for targeted cancer therapies — treatments that are designed and chosen using genetic or molecular profiling. Existing hospital IT doesn’t support the robust infrastructure and clinical workflows required to treat patients with precision medicine at scale, but Syapse centralizes and organizes patient data to clinicians at the point of care. Syapse offers a variety of solutions for oncologists that allow them to access the full scope of patient data longitudinally, view recommended treatments or clinical trials for similar patients, and track outcomes over time. These solutions are helping health systems across the country to improve patient outcomes by offering the most innovative care to cancer patients.

Leading health systems such as Stanford Health Care, Providence St. Joseph Health, and Intermountain Healthcare are using Syapse to improve patient outcomes, streamline clinical workflows, and scale their precision medicine programs. A group of experts known as the Molecular Tumor Board (MTB) reviews complex cases and evaluates patient data, documents notes, and disseminates treatment recommendations to the treating physician. Syapse also provides reports that give health system staff insight into their institution’s oncology care, which can be used toward quality improvement, business goals, and understanding variables in the oncology service line.

Syapse uses Amazon Virtual Private Cloud, Amazon EC2 Dedicated Instances, and Amazon Elastic Block Store to build a high-performance, scalable, and HIPAA-compliant data platform that enables health systems to make precision medicine part of routine cancer care for patients throughout the country.

Be sure to check out the Syapse blog to learn more and also their recent video on the #StartupsOnAir video series where they discuss their product, HIPAA compliance, and more about how they are using AWS.

Thank you for checking out another month of awesome hot startups!

-Tina Barr

 

AWS and Healthcare: View From the Floor of HIMSS17

Post Syndicated from Ana Visneski original https://aws.amazon.com/blogs/aws/aws-and-healthcare-view-from-the-floor-of-himss17/

Jordin Green is our guest writer today, with an inside view from the floor of HIMSS17.

-Ana

HIMMS1Empathy. It’s not always a word you hear associated with technology but one might argue it should be a central tenet for the application of technology in healthcare, and I was reminded of that fact as I wandered the halls representing AWS at HIMSS17, the Healthcare Information and Management System Society annual meeting.

At Amazon, we’re taught to obsess over our customers, but this obsession takes on a new level of responsibility when those customers are directly working on improving the lives of patients and the overall wellness of society. Thinking about the challenges that healthcare professionals are dealing with every day drives home how important it is for AWS to ensure that using the cloud in healthcare is as frictionless as possible. So with that in mind I wanted to share some of the things I saw in and around HIMSS17 regarding healthcare and AWS.

I started my week at the HIMSS Cloud Computing Forum, which was a new full-day HIMSS pre-day focused on educating the healthcare community on cloud. I was particularly struck by the breadth of cloud use cases being explored throughout the industry, even compared to a few years ago. The program featured presentations on cloud-based care coordination, precision medicine, and security. Perhaps one of the most interesting presentations came from Jessica Kahn from the Center for Medicare & Medicaid Services (CMS), talking about the analytics platform that CMS has built in the cloud along with Nuna. Jessica talked about how a cloud-based platform allows CMS to make decisions based on data that is a month old, rather than a year or years old. Additionally, using an automated, rules-based approach, policymakers can directly query the data without having to rely on developers, bringing agility. Nuna has already gained a number of insights from hosting this de-identified Medicaid data for policy research, and is now looking to expand its services to private insurance.

The exhibition opened on Monday, and I was really excited to talk to customers about the new Healthcare & Life Sciences category in the AWS Marketplace. AWS Marketplace is a managed and curated software catalog that helps customers innovate faster and reduce costs, by making it easy to discover, evaluate, procure, immediately deploy and manage 3rd party software solutions. When a customer purchases software via the Marketplace, all of the infrastructure needed to run on AWS is deployed automatically, using the same pay-as-you-go pricing model that AWS uses. Creation of a dedicated category of healthcare is a huge step forward in making it easier for our customers to deploy cloud-based solutions. Our new category features telehealth solutions, products for managing HIPAA compliance, and products that can be used for revenue cycle management from AWS Partner Network (APN) such as PokitDok. We’re just getting started with this category; look for new additions throughout the year.

Later in the week, I tried to spend time at the number of APN Partners exhibiting at HIMSS this year, and it’s safe to say our ecosystem also had lots of moments to shine. Orion Health announced that they will migrate their Amadeus precision medicine platform to the AWS Cloud. Orion has been deploying on top of AWS for a while now, including notably the California-wide Health Information Exchange CalINDEX. Amadeus currently manages 110 million patient records; the migration will represent a significant volume of clinical data running on AWS. New APN Partner Merck announced a new Alexa skill challenge, asking developers to come up with new, innovative ways to use Alexa in the management of chronic disease. Healthcare Competency Partner ClearDATA announced its new fully-managed Containers-as-a-Service product, which simplifies development of healthcare applications by providing developers with a HIPAA-compliant environment for building, testing, and deployment.

This is only a small sample of the activity going on at HIMSS this year, and it’s impossible to capture everything in one post. You learn more about healthcare on AWS on our Cloud Computing in Healthcare page. Nonetheless, after spending four days diving in to healthcare IT, it was great to see how AWS is enabling our healthcare customers and partners deliver solutions that are impacting millions of lives across the globe.

–Jordin Green