Monthly Archives: May 2021

Medical data is highly contextual and heavily multi-modal, in which each data silo is treated separately. To bridge different data, a knowledge graph-based approach integrates data across domains and helps represent the complex representation of scientific knowledge more naturally. For example, three components of major electronic health records (EHR) are diagnosis codes, primary notes, and specific medications. Because these are represented in different data silos, secondary use of these documents for accurately identifying patients with a specific observable trait is a crucial challenge. By connecting those different sources, subject matter experts have a richer pool of data to understand how different concepts such as diseases and symptoms interact with one another and help conduct their research. This ultimately helps healthcare and life sciences researchers and practitioners create better insights from the data for a variety of use cases, such as drug discovery and personalized treatments.

In this post, we use Amazon HealthLake to export EHR data in the Fast Healthcare Interoperability Resources (FHIR) data format. We then build a knowledge graph based on key entities extracted and harmonized from the medical data. Amazon HealthLake also extracts and transforms unstructured medical data, such as medical notes, so it can be searched and analyzed. Together with Amazon Kendra and Amazon Neptune, we allow domain experts to ask a natural language question, surface the results and relevant documents, and show connected key entities such as treatments, inferred ICD-10 codes, medications, and more across records and documents. This allows for easy analysis of co-occurrence of key entities, co-morbidities analysis, and patient cohort analysis in an integrated solution. Combining effective search capabilities and data mining through graph networks reduces time and cost for users to find relevant information around patients and improve knowledge serviceability surrounding EHRs. The code base for this post is available on the GitHub repo.

Solution overview

In this post, we use the output from Amazon HealthLake for two purposes.

First, we index EHRs into Amazon Kendra for semantic and accurate document ranking out of patient notes, which help improve physician efficiency identifying patient notes and compare it with other patients sharing similar characteristics. This shifts from using a lexical search to a semantic search that introduces context around the query, which results in better search output (see the following screenshot).

Second, we use Neptune to build knowledge graph applications for users to view metadata associated with patient notes in a more simple and normalized view, which allows us to highlight the important characteristics stemming from a document (see the following screenshot).

The following diagram illustrates our architecture.

The steps to implement the solution are as follows:

1. Create and export Amazon HealthLake data.
2. Extract patient visit notes and metadata.
3. Load patient notes data into Amazon Kendra.
4. Load the data into Neptune.
5. Set up the backend and front end to run the web app.

Create and export Amazon HealthLake data

As a first step, create a data store using Amazon HealthLake either via the Amazon HealthLake console or the AWS Command Line Interface (AWS CLI). For this post, we focus on the AWS CLI approach.

1. We use AWS Cloud9 to create a data store with the following code, replacing <<your data store name >> with a unique name:

aws healthlake create-fhir-datastore --region us-east-1 --datastore-type-version R4 --preload-data-config PreloadDataType="SYNTHEA" --datastore-name "<<your_data_store_name>>"

The preceding code uses a preloaded dataset from Synthea, which is supported in FHIR version R4, to explore how to use Amazon HealthLake output. Running the code produces a response similar to the following code, and this step takes a few minutes to complete (approximately 30 minutes at the time of writing):

{
	"DatastoreEndpoint": "https://healthlake.us-east-1.amazonaws.com/datastore/<<your_data_store_id>>/r4/",
	"DatastoreArn": "arn:aws:healthlake:us-east-1:<<your_AWS_account_number>>:datastore/fhir/<<your_data_store_id>>",
	"DatastoreStatus": "CREATING",
	"DatastoreId": "<<your_data_store_id>>"
}

You can check the status of completion either on the Amazon HealthLake console or in the AWS Cloud9 environment.

2. To check the status in AWS Cloud9, use the following code to check the status and wait until DatastoreStatus changes from CREATING to ACTIVE:

aws healthlake describe-fhir-datastore --datastore-id "<<your_data_store_id>>" --region us-east-1

3. When the status changes to ACTIVE, get the role ARN from the HEALTHLAKE-KNOWLEDGE-ANALYZER-IAMROLE stack in AWS CloudFormation, associated with the physical ID AmazonHealthLake-Export-us-east-1-HealthDataAccessRole, and copy the ARN in the linked page.
4. In AWS Cloud9, use the following code to export the data from Amazon HealthLake to the Amazon Simple Storage Service (Amazon S3) bucket generated from AWS Cloud Development Kit (AWS CDK) and note the job-id output:

aws healthlake start-fhir-export-job --output-data-config S3Uri="s3://hl-synthea-export-<<your_AWS_account_number>>/export-$(date +"%d-%m-%y")" --datastore-id <<your_data_store_id>> --data-access-role-arn arn:aws:iam::<<your_AWS_account_number>>:role/AmazonHealthLake-Export-us-east-1-HealthKnoMaDataAccessRole

5. Verify that the export job is complete using the following code with the job-id obtained from the last code you ran. (when the export is complete, JobStatus in the output states COMPLETED):

aws healthlake describe-fhir-export-job --datastore-id <<your_data_store_id>> --job-id <<your_job_id>>

Extract patient visit notes and metadata

The next step involves decoding patient visits to obtain the raw texts. We will import the following file DocumentReference-0.ndjson (shown in the following screenshot of S3) from the Amazon HealthLake export step we previously completed into the CDK deployed Amazon SageMaker notebook instance. First, save the notebook provided from the Github repo into the SageMaker instance. Then, run the notebook to automatically locate and import the DocumentReference-0.ndjson files from S3.

For this step, use the resourced SageMaker to quickly run the notebook. The first part of the notebook creates a text file that contains notes from each patient’s visit and is saved to an Amazon S3 location. Because multiple visits could exist for a single patient, a unique identification combines the patient unique ID and the visit ID. These patients’ notes are used to perform semantic search against using Amazon Kendra.

The next step in the notebook involves creating triples based on the automatically extracted metadata. By creating and saving the metadata in an Amazon S3 location, an AWS Lambda function gets triggered to generate the triples surrounding the patient visit notes.

Load patient notes data into Amazon Kendra

The text files that are uploaded in the source path of the S3 bucket need to be crawled and indexed. For this post, a developer edition is created during the AWS CDK deployment, so the index is created to connect the raw patient notes.

1. On the AWS CloudFormation console under the HEALTHLAKE-KNOWLEDGE-ANALYZER-CORE stack, search for kendra on the Resources tab and take note of the index ID and data source ID (copy the first part of the physical ID before the pipe ( | )).

2. Back in AWS Cloud9, run the following command to synchronize the patient notes in Amazon S3 to Amazon Kendra:

aws kendra start-data-source-sync-job --id <<data_source_id_2nd_circle>> --index-id <<index_id_1st_ circle>>

3. You can verify when the sync status is complete by running the following command:

aws kendra describe-data-source --id <<data_source_id_2nd_circle>> --index-id <<index_id_1st_circle>>

Because the ingested data is very small, it should immediately show that Status is ACTIVE upon running the preceding command.

Load the data into Neptune

In this next step, we access the Amazon Elastic Compute Cloud (Amazon EC2) instance that was spun up and load the triples from Amazon S3 into Neptune using the following code:

curl -X POST 
    -H 'Content-Type: application/json' 
    https://healthlake-knowledge-analyzer-vpc-and-neptune-neptunedbcluster.cluster-<<your_unique_id>>.us-east-1.neptune.amazonaws.com:8182/loader -d '
{
    "source": "s3://<<your_Amazon_S3_bucket>>/stdized-data/neptune_triples/nquads/",
    "format": "nquads",
    "iamRoleArn": "arn:aws:iam::<<your_AWS_account_number>>:role/KNOWLEDGE-ANALYZER-IAMROLE-ServiceRole",
    "region": "us-east-1",
    "failOnError": "TRUE"
}'

Set up the backend and front end to run the web app

The preceding step should take a few seconds to complete. In the meantime, configure the EC2 instance to access the web app. Make sure to have both Python and Node installed in the instance.

1. Run the following code in the terminal of the instance:

sudo iptables -t nat -I PREROUTING -p tcp --dport 80 -j REDIRECT --to-ports 3000

This routes the public address to the deployed app.

2. Copy the two folders titled ka-webapp and ka-server-webapp and upload them to a folder named dev in the EC2 instance.
3. For the front end, create a screen by running the following command:

screen -S back 

4. In this screen, change the folder to ka-webapp and run npm install.
5. After installation, go into the file .env.development and place the Amazon EC2 public IPv4 address and save the file.
6. Run npm start and then detach the screen.
7. For the backend, create another screen by entering:

screen -S back

8. Change the folder to ka-server-webapp and run pip install -r requirements.txt.
9. When the libraries are installed, enter the following code:

./run.sh

10. Detach from the current screen, and using any browser, go the Amazon EC2 Public IPv4 address to access the web app.

Try searching for a patient diagnosis and choose a document link to visualize the knowledge graph of that document.

Next steps

In this post, we integrate data output from Amazon HealthLake into both a search and graph engine to semantically search relevant information and highlight important entities linked to documents. You can further expand this knowledge graph and link it to other ontologies such as MeSH and MedDRA.

Furthermore, this provides a foundation to further integrate other clinical datasets and expand this knowledge graph to build a data fabric. You can make queries on historical population data, chaining structured and language-based searches for cohort selection to correlate disease with patient outcome.

Clean up

To clean up your resources, complete the following steps:

1. To delete the stacks created, enter the following commands in the order given to properly remove all resources:

$ cdk destroy HEALTHLAKE-KNOWLEDGE-ANALYZER-UPDATE-CORE
$ cdk destroy HEALTHLAKE-KNOWLEDGE-ANALYZER-WEBAPP
$ cdk destroy HEALTHLAKE-KNOWLEDGE-ANALYZER-CORE

2. While the preceding commands are in progress, delete the Amazon Kendra data source that was created:

$ cdk destroy HEALTHLAKE-KNOWLEDGE-ANALYZER-VPC-AND-NEPTUNE
$ cdk destroy HEALTHLAKE-KNOWLEDGE-ANALYZER-IAMROLE
$ aws healthlake delete-fhir-datastore --datastore-id <<your_data_store_id>> 

3. To verify it’s been deleted, check the status by running the following command:

$ aws healthlake describe-fhir-datastore --datastore-id "<<your_data_store_id>>" --region us-east-1

4. Check the AWS CloudFormation console to ensure that all associated stacks starting with HEALTHLAKE-KNOWLEDGE-ANALYZER have all been deleted successfully.

Conclusion

Amazon HealthLake provides a managed service based on the FHIR standard to allow you to build health and clinical solutions. Connecting the output of Amazon HealthLake to Amazon Kendra and Neptune gives you the ability to build a cognitive search and a health knowledge graph to power your intelligent application.

Building on top of this approach can enable researchers and front-line physicians to easily search across clinical notes and research articles by simply typing their question into a web browser. Every clinical evidence is tagged, indexed, and structured using machine learning to provide evidence-based topics on things like transmission, risk factors, therapeutics, and incubation. This particular functionality is tremendously valuable for clinicians or scientists because it allows them to quickly ask a question to validate and advance their clinical decision support or research.

Try this out on your own! Deploy this solution using Amazon HealthLake in your AWS account by deploying the example on GitHub.

About the Authors

Prithiviraj Jothikumar, PhD, is a Data Scientist with AWS Professional Services, where he helps customers build solutions using machine learning. He enjoys watching movies and sports and spending time to meditate.

Phi Nguyen is a solutions architect at AWS helping customers with their cloud journey with a special focus on data lake, analytics, semantics technologies and machine learning. In his spare time, you can find him biking to work, coaching his son’s soccer team or enjoying nature walk with his fami

Parminder Bhatia is a science leader in the AWS Health AI, currently building deep learning algorithms for clinical domain at scale. His expertise is in machine learning and large scale text analysis techniques in low resource settings, especially in biomedical, life sciences and healthcare technologies. He enjoys playing soccer, water sports and traveling with his family.

Garin Kessler is a Senior Data Science Manager at Amazon Web Services, where he leads teams of data scientists and application architects to deliver bespoke machine learning applications for customers. Outside of AWS, he lectures on machine learning and neural language models at Georgetown. When not working, he enjoys listening to (and making) music of questionable quality with friends and family.

Dr. Taha Kass-Hout is Director of Machine Learning and Chief Medical Officer at Amazon Web Services, and leads our Health AI strategy and efforts, including Amazon Comprehend Medical and Amazon HealthLake. Taha is also working with teams at Amazon responsible for developing the science, technology, and scale for COVID-19 lab testing. A physician and bioinformatician, Taha served two terms under President Obama, including the first Chief Health Informatics officer at the FDA. During this time as a public servant, he pioneered the use of emerging technologies and cloud (CDC’s electronic disease surveillance), and established widely accessible global data sharing platforms, the openFDA, that enabled researchers and the public to search and analyze adverse event data, and precisionFDA (part of the Presidential Precision Medicine initiative).

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Whether you’re watching a live broadcast of your favorite soccer team, having a video chat with a vendor, or calling your bank about a loan payment, streaming speech content is everywhere. You can apply a streaming transcription service to generate subtitles for content understanding and accessibility, to create metadata to enable search, or to extract insights for call analytics. These transcription services process streaming audio content and generate partial transcription results until it provides a final transcription for a segment of continuous speech. However, some words or phrases in these partial results might change, as the service further understands the context of the audio.

We’re happy to announce that Amazon Transcribe now allows you to enable and configure partial results stabilization for streaming audio transcriptions. Amazon Transcribe is an automatic speech recognition (ASR) service that enables developers to add real-time speech-to-text capabilities into their applications for on-demand and streaming content. Instead of waiting for an entire sentence to be transcribed, you can now control the stabilization level of partial results. Transcribe offers 3 settings: High, Medium and Low. Setting the stabilization “High” allows a greater portion of the partial results to be fixed with only the last few words changing during the transcription process. This feature helps you have more flexibility in your streaming transcription workflows based on the user experience you want to create.

In this post, we walk through the benefits of this feature and how to enable it via the Amazon Transcribe console or the API.

How partial results stabilization works

Let’s dive deeper into this with an example.

During your daily conversations, you may think you hear a certain word or phrase, but later realize that it was incorrect based on additional context. Let’s say you were talking to someone about food, and you heard them say “Tonight, I will eat a pear…” However, when the speaker finishes, you realize they actually said “Tonight I will eat a pair of pancakes.” Just as humans may change our understanding based on the information at hand, Amazon Transcribe uses machine learning (ML) to self-correct the transcription of streaming audio based on the context it receives. To enable this, Amazon Transcribe uses partial results.

During the streaming transcription process, Amazon Transcribe outputs chunks of the results with an isPartial flag. Results with this flag marked as true are the ones that Amazon Transcribe may change in the future depending on the additional context received. After Amazon Transcribe classifies that it has sufficient context to be over a certain confidence threshold, the results are stabilized and the isPartial flag for that specific partial result is marked false. The window size of these partial results could range from a few words to multiple sentences depending on the stream context.

The following image displays how the partial results are generated (and edited) in Amazon Transcribe for streaming transcription.

Results stabilization enables more control over the latency and accuracy of transcription results. Depending on the use case, you may prioritize one over the other. For example, when providing live subtitles, high stabilization of results may be preferred because speed is more important than accuracy. On the other hand for use cases like content moderation, lower stabilization is preferred because accuracy may be more important than latency.

A high stability level enables quicker stabilization of transcription results by limiting the window of context for stabilizing results, but can lead to lower overall accuracy. On the other hand, a low stability level leads to more accurate transcription results, but the partial transcription results are more likely to change.

With the streaming transcription API, you can now control the stability of the partial results in your transcription stream.

Now let’s look at how to use the feature.

Access partial results stabilization via the Amazon Transcribe console

To start using partial results stabilization on the Amazon Transcribe console, complete the following steps:

1. On the Amazon Transcribe console, make sure you’re in a Region that supports Amazon Transcribe Streaming.

For this post, we use us-east-1.

2. In the navigation pane, choose Real-time transcription.
3. Under Additional settings, enable Partial results stabilization.

4. Select your stability level.

You can choose between three levels:

• High – Provides the most stable partial transcription results with lower accuracy compared to Medium and Low settings. Results are less likely to change as additional context is gathered.
• Medium – Provides partial transcription results that have a balance between stability and accuracy
• Low – Provides relatively less stable partial transcription results with higher accuracy compared to High and Medium settings. Results get updated as additional context is gathered and utilized.

5. Choose Start streaming to play a stream and check the results.

Access partial results stabilization via the API

In this section, we demonstrate streaming with HTTP/2. You can enable your preferred level of partial results stabilization in an API request.

You enable this feature via the enable-partial-results-stabilization flag and the partial-results-stability level input parameters:

POST /stream-transcription HTTP/2 
x-amzn-transcribe-language-code: LanguageCode 
x-amzn-transcribe-sample-rate: MediaSampleRateHertz 
x-amzn-transcribe-media-encoding: MediaEncoding 
x-amzn-transcribe-session-id: SessionId 
x-amzn-transcribe-enable-partial-results-stabilization= true
x-amzn-transcribe-partial-results-stability = low | medium | high

Enabling partial results stabilization introduces the additional parameter flag Stable in the API response at the item level in the transcription results. If a partial results item in the streaming transcription result has the Stable flag marked as true, the corresponding item transcription in the partial results doesn’t change irrespective of any subsequent context identified by Amazon Transcribe. If the Stable flag is marked as false, there is still a chance that the corresponding item may change in the future, until the IsPartial flag is marked as false.

The following code shows our API response:

{
    "Alternatives": [
        {
            "Items": [
                {
                    "Confidence": 0,
                    "Content": "Amazon",
                    "EndTime": 1.22,
                    "Stable": true,
                    "StartTime": 0.78,
                    "Type": "pronunciation",
                    "VocabularyFilterMatch": false
                },
                {
                    "Confidence": 0,
                    "Content": "is",
                    "EndTime": 1.63,
                    "Stable": true,
                    "StartTime": 1.46,
                    "Type": "pronunciation",
                    "VocabularyFilterMatch": false
                },
                {
                    "Confidence": 0,
                    "Content": "the",
                    "EndTime": 1.76,
                    "Stable": true,
                    "StartTime": 1.64,
                    "Type": "pronunciation",
                    "VocabularyFilterMatch": false
                },
                {
                    "Confidence": 0,
                    "Content": "largest",
                    "EndTime": 2.31,
                    "Stable": true,
                    "StartTime": 1.77,
                    "Type": "pronunciation",
                    "VocabularyFilterMatch": false
                },
                {
                    "Confidence": 1,
                    "Content": "rainforest",
                    "EndTime": 3.34,
                    "Stable": true,
                    "StartTime": 2.4,
                    "Type": "pronunciation",
                    "VocabularyFilterMatch": false
                },      
            ],
            "Transcript": "Amazon is the largest rainforest "
        }
    ],
    "EndTime": 4.33,
    "IsPartial": false,
    "ResultId": "f4b5d4dd-b685-4736-b883-795dc3f7f636",
    "StartTime": 0.78
}

Conclusion

This post introduces the recently launched partial results stabilization feature in Amazon Transcribe. For more information, see the Amazon Transcribe Partial results stabilization documentation.

To learn more about the Amazon Transcribe Streaming Transcription API, check out Using Amazon Transcribe streaming With HTTP/2 and Using Amazon Transcribe streaming with WebSockets.

About the Author

Alex Chirayath is an SDE in the Amazon Machine Learning Solutions Lab. He helps customers adopt AWS AI services by building solutions to address common business problems.

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AWS is excited to announce that The Washington Post is integrating Amazon Polly to provide their readers with audio access to stories across The Post’s entire spectrum of web and mobile platforms, starting with technology stories. Amazon Polly is a service that turns text into lifelike speech, allowing you to create applications that talk, and build entirely new categories of speech-enabled products. Post subscribers live busy lives with limited time to read the news. The goal is to unlock the Post’s world-class written journalism in audio form and give readers a convenient way to stay up to date on the news, like listening while doing other things.

In The Post’s announcement, Kat Down Mulder, managing editor says, “Whether you’re listening to a story while multitasking or absorbing a compelling narrative while on a walk, audio unlocks new opportunities to engage with our journalism in more convenient ways. We saw that trend throughout last year as readers who listened to audio articles on our apps engaged more than three times longer with our content. We’re doubling-down on our commitment to audio and will be experimenting rapidly and boldly in this space. The full integration of Amazon Polly within our publishing ecosystem is a big step that offers readers this powerful convenience feature at scale, while ensuring a high-quality and consistent audio experience across all our platforms for our subscribers and readers.”

Integrating Amazon Polly into The Post’s publishing workflow has been easy and straightforward. When an article is ready for publication, the written content management system (CMS) publishes the text article and simultaneously sends the text to the audio CMS, where the article text is processed by Amazon Polly to produce an audio recording of the article. The audio is delivered as an mp3 and published in conjunction with the written portion of the article.

Figure 1 High-level architecture Washington Post article creation

Last year, The Post began testing article narration using the text-to-speech, accessibility capabilities in iOS and Android operating systems. While there were promising signs around engagement, some noted that the voices sounded robotic. The Post started testing other options and ended up choosing Amazon Polly because of its high-quality automated voices. “We’ve tested users’ perceptions to both human and automated voices and found high levels of satisfaction with Amazon Polly’s offering. Integrating Amazon Polly into our publishing workflow also gives us the ability to offer a consistent listening experience across platforms and experiment with new functions that we believe our subscribers will enjoy.” says Ryan Luu, senior product manager at The Post.

Over the coming months, The Post will be adding voice support for new sections, new languages and better usability. “We plan to introduce new features like more playback controls, text highlighting as you listen, and audio versions of Spanish articles,” said Luu. “We also hope to give readers the ability to create audio playlists to make it easy for subscribers to queue up stories they’re interested in and enjoy that content on the go.”

Amazon Polly is a text-to-speech service that powers audio access to news articles for media publishers like Gannett (the publisher of USA Today), The Globe and Mail (the biggest newspaper in Canada), and leading publishing companies such as BlueToad and Trinity Audio. In addition, Amazon Polly provides natural sounding voices in a variety of languages and personas to give content a voice in other sectors such as education, healthcare, and gaming.

For more information, see What Is Amazon Polly? and log in to the Amazon Polly console to try it out for free. To experience The Post’s new audio articles, listen to the story “Did you get enough steps in today? Maybe one day you’ll ask your ‘smart’ shirt.”

About the Author

Esther Lee is a Product Manager for AWS Language AI Services. She is passionate about the intersection of technology and education. Out of the office, Esther enjoys long walks along the beach, dinners with friends and friendly rounds of Mahjong.

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