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Build patient outcome prediction applications using Amazon HealthLake and Amazon SageMaker

Healthcare data can be challenging to work with and AWS customers have been looking for solutions to solve certain business challenges with the help of data and machine learning (ML) techniques. Some of the data is structured, such as birthday, gender, and marital status, but most of the data is unstructured, such as diagnosis codes…

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Healthcare data can be challenging to work with and AWS customers have been looking for solutions to solve certain business challenges with the help of data and machine learning (ML) techniques. Some of the data is structured, such as birthday, gender, and marital status, but most of the data is unstructured, such as diagnosis codes or physician’s notes. This data is designed for human beings to understand, but not for computers to comprehend. The key challenges of using healthcare data are as follows:

  • How to effectively use both structured and unstructured data to get a complete view of the data
  • How to intuitively interpret the prediction results

With the rise of AI/ML technologies, solving these challenges became possible.

One relevant use case is patient outcome prediction, which includes acute or chronic condition-triggered hospital visits or readmission predictions, disease progression predictions within a certain observation window, and so on. Healthcare providers, payors, and pharmaceutical companies can use prediction results to recommend early intervention, improve outreach communication, improve patient care experience, and reduce overall cost.

In this post, we show you an example of building a deep learning based patient outcome prediction model. We build the model in Amazon SageMaker with MIMIC-III data stored in Amazon HealthLake and turn it into a lightweight application for visualization and interpretability. The prediction target for this example is mortality prediction within 90 days after ICU discharge. You can modify the target variable to suit your needs.

Amazon HealthLake helps make sense of health data

HealthLake is a HIPAA-eligible service that enables healthcare providers, health insurance companies, and pharmaceutical companies to store, transform, query, and analyze health data at petabyte scale.

The data source we exported from the HealthLake API is called MIMIC-III [1]. It’s a large, freely available database comprised of deidentified health-related data associated with over 40,000 patients who stayed in critical care units. The database includes information such as demographics, vital signs, lab test results, procedures, medications, caregiver notes, imaging reports, and mortality. We can’t share the data in this post due to license restrictions, but you can visit MIMIC’s official website to request data access.

HealthLake automatically extracts clinical entities and links ICD-10-CM and RxNorm codes to unstructured text such as discharge notes when the text is stored in HealthLake as a Fast Healthcare Interoperable Resource (FHIR) DocumentReference type. The extracted entities are added back onto the FHIR DocumentReference resource as a FHIR extension. Text embedded in the DocumentReference should be base64 encoded. When building the predictive models, we can combine the extracted information with other structured data and get a more holistic view of the patient’s medical history.

Overview of solution

The following architecture diagram illustrates the model training pipeline, inference pipeline, and information-rendering front end.

We use the HealthLake export API to export the normalized data to an Amazon Simple Storage Service (Amazon S3) bucket. Then we use an AWS Glue crawler to create a Data Catalog. We can use Amazon Athena with the Data Catalog to run SQL-like queries against the exported data. Unstructured data of patient records gets processed separately to extract indexed data and combine it with other structured information. Then we use a SageMaker notebook with TensorFlow containers to train a custom convolutional neural network model. The model artifact is saved to an S3 bucket and later is used to test model performance on unseen data. Finally, we run inference on the model using SageMaker batch transform and save the results to Amazon S3. We also develop visualization components and render them via Amazon API Gateway to improve the model’s interpretability.

In this post, we walk you through the following steps:

  1. Create training and testing datasets.
  2. Use embedding techniques for a richer representation of the unstructured data.
  3. Train the model.
  4. Evaluate our results.
  5. Visualize the results with custom UI components.

Create training and testing datasets

First, we create a binary variable for the target—mortality within a 90-day window after discharge. A patient may have multiple records with the target variable value as 0 before this patient’s mortality status is set to 1. This situation also applies to many other patient outcome prediction target variables. We therefore split the data by patient_id into training, validation, and testing datasets to prevent information leakage. This way, a single patient’s multiple records don’t appear in more than one dataset category.

We first put aside 20% of the patients for testing purposes, and treat these patient records as never seen by the algorithm. Among the remaining 80% patients, we take another 80% of the data for training and 20% for validation. We upload these datasets to our S3 bucket for later use.

Use embedding techniques

Traditional ML methods may use frequency count based encoding techniques such as term frequency-inverse document frequency (TF-IDF). In this post, we use embedding techniques that take advantage of a richer representation of the unstructured data by learning relationships between different medical codes.

We first take in a sequence of medical codes and use skip-gram to learn the relationships between different codes. The learned embedding for each medical code is typically an n-length vector (such as 300) that characterizes the individual element. These dimensions usually don’t have explicit meanings, however, similar medical concepts should be projected closer to one another in the feature space. We learned such vectors for all the vocabularies during training and stacked them together as a matrix. We later use this embedding matrix to train a convolutional neural network model and perform testing on unseen data.

Train the model

We first define the structure of the neural network and then use a SageMaker-hosted TensorFlow training image to train our model. The layers are defined as follows:

  • Embedding layer – Takes in raw medical code sequences and converts each individual code into embeddings
  • Convolutional layer – Takes in the embeddings and convolves with tunable filters
  • Pooling layer – Applies aggregation computations to reduce the size for the next layers’ input
  • Dropout layer – Randomly turns off connections to reduce overfitting
  • Concatenation layer – Combines the processed information from the previous layers with structured information such as patient age or gender
  • Fully connected layer with sigmoid activation – Outputs the final prediction probabilities

During training, we use these prediction probabilities to calculate metrics and guide the direction of the training process. During testing, these probabilities are output as a file on Amazon S3.

When the training process is complete, we save the model artifact and upload it to an S3 bucket for later use.

Review results

The following visualization shows the ROC (Receiver Operating Characteristics) curve and classification report on the test data.

The ROC curve shows the model’s performance at different thresholds. The AUC (Area Under the Curve) for the ROC curve is 0.82, which measures the model’s ability to separate different target classes. The classification report gives you an overview of the model’s precision, recall, and F1 score for each class. The weighted average F1 score for the model is 0.74.

Visualization with custom UI components

We can visualize the prediction results in many ways. For this post, we only demonstrate how to render SHAP (SHapley Additive exPlanations) values to improve the model’s interpretability. SHAP is a game theoretic approach to explain the output of an ML model. The visualization can show the details of each prediction’s contributing factors so that you can intuitively understand what features are pushing the predicted probability higher (towards 1) or lower (towards 0) from the base value.

We first define an HTML template and keep adding visualization components into the template. We then upload the HTML file to an S3 bucket and set up an AWS Lambda function to retrieve the HTML content, and the content is sent to an API Gateway to render a webpage.

Set up HTML templates

We can define an HTML template with empty code blocks in it with the following code:

Dashboard

Indicators

{shap_value}

We can generate the {} later via Python or JavaScript libraries and plug the code into the blocks.

Create visualization components

An example of creating a SHAP value visualization might look like the following code:

explainer = shap.KernelExplainer (tf_model, X_train) shap_values = explainer.shap_values(X_test, nsamples=100) shap.initjs() shap.force_plot(explainer.expected_value[0], shap_values[0][0,:], X_test.iloc[0,:])

This can generate an intuitive explanation of drivers behind the predictions. As shown in the following visualization, the red tickers are driving the probability of a patient’s outcome prediction to the higher end (towards 1), and the blue tickers are driving the probability to the lower end (towards 0). As a result, this patient has a probability of 0.34 compared to the training cohort base value of 0.4481. Therefore, this patient has a lower chance of being positive on the target variable.

Create a Lambda function to parse the HTML file

An example Lambda function can be as simple as the following code:

import json import boto3 def lambda_handler(event, context): s3 = boto3.client(‘s3′) response = s3.get_object(Bucket=’‘, Key=’‘) return response[‘Body’].read().decode(‘utf-8’)

The purpose of this function is to retrieve the information that needs to be rendered without exposing the Amazon S3 resources to the public, and send the information to an API Gateway.

Create an API Gateway to render the HTML file

We can use the AWS Cloud Development Kit (AWS CDK) to automate these settings. For example:

api = apigw.LambdaRestApi( self, ‘visualization’, handler=lambda_handler, proxy=False) integration = apigw.LambdaIntegration( lambda_handler, proxy=False, passthrough_behavior=apigw.PassthroughBehavior.WHEN_NO_TEMPLATES, integration_responses=[{ “statusCode”: “200”, “responseTemplates”: {“text/html”: “$input.path(‘$’)”}, “responseParameters”: { “method.response.header.Content-Type”: “‘text/html'”} }])

The integration_responses part ensures that the returned content is rendered correctly as HTML by API Gateway. When the API Gateway is deployed, you get an invoke URL. You can copy and paste this URL into a web browser to check the visualization result.

Conclusion

In this post, we demonstrated how to use Amazon SageMaker and Amazon HealthLake to build a deep learning model to solve a healthcare and life sciences challenge and interpret the results via visualization techniques. With this solution, hospitals can better care for patients and provide appropriate intervention by predicting patient outcomes. We demonstrated this solution for a mortality prediction within 90 days after ICU discharge, you can apply the same method to other patient outcome prediction use cases.

HealthLake makes it easy to work with health data and extract relevant data points from unstructured clinical texts. Deep learning modeling techniques give us options to build more accurate models with less feature engineering effort, and AWS technologies make it possible to visualize model interpretations with a lightweight front-end solution.

To learn more about HealthLake, see Amazon HealthLake resources and Making sense of your health data with Amazon HealthLake. For a hands-on tutorial, visit our Amazon HealthLake workshop. For more examples using HealthLake and population health, see Population health applications with Amazon HealthLake – Part 1: Analytics and monitoring using Amazon QuickSight.

References

[1] MIMIC-III, a freely accessible critical care database. Johnson AEW, Pollard TJ, Shen L, Lehman L, Feng M, Ghassemi M, Moody B, Szolovits P, Celi LA, and Mark RG. Scientific Data (2016). DOI: 10.1038/sdata.2016.35. Available from: http://www.nature.com/articles/sdata201635

About the Authors

 Shuai Cao is a Data Scientist in the Professional Services team at Amazon Web Services. His expertise is building machine learning applications at scale for healthcare and life sciences customers. Outside of work, he loves traveling around the world and playing dozens of different instruments.

 

 

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.

 

 

Kartik Kannapur is a Data Scientist with AWS Professional Services. He holds a master’s degree in Applied Mathematics and Statistics from Stony Brook University and focuses on using machine learning to solve customer business problems.

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AWS Week in Review – May 16, 2022

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS! I had been on the road for the last five weeks and attended many of the AWS Summits in Europe. It was great to talk to so many of you…

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This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

I had been on the road for the last five weeks and attended many of the AWS Summits in Europe. It was great to talk to so many of you in person. The Serverless Developer Advocates are going around many of the AWS Summits with the Serverlesspresso booth. If you attend an event that has the booth, say “Hi ” to my colleagues, and have a coffee while asking all your serverless questions. You can find all the upcoming AWS Summits in the events section at the end of this post.

Last week’s launches
Here are some launches that got my attention during the previous week.

AWS Step Functions announced a new console experience to debug your state machine executions – Now you can opt-in to the new console experience of Step Functions, which makes it easier to analyze, debug, and optimize Standard Workflows. The new page allows you to inspect executions using three different views: graph, table, and event view, and add many new features to enhance the navigation and analysis of the executions. To learn about all the features and how to use them, read Ben’s blog post.

Example on how the Graph View looks

Example on how the Graph View looks

AWS Lambda now supports Node.js 16.x runtime – Now you can start using the Node.js 16 runtime when you create a new function or update your existing functions to use it. You can also use the new container image base that supports this runtime. To learn more about this launch, check Dan’s blog post.

AWS Amplify announces its Android library designed for Kotlin – The Amplify Android library has been rewritten for Kotlin, and now it is available in preview. This new library provides better debugging capacities and visibility into underlying state management. And it is also using the new AWS SDK for Kotlin that was released last year in preview. Read the What’s New post for more information.

Three new APIs for batch data retrieval in AWS IoT SiteWise – With this new launch AWS IoT SiteWise now supports batch data retrieval from multiple asset properties. The new APIs allow you to retrieve current values, historical values, and aggregated values. Read the What’s New post to learn how you can start using the new APIs.

AWS Secrets Manager now publishes secret usage metrics to Amazon CloudWatch – This launch is very useful to see the number of secrets in your account and set alarms for any unexpected increase or decrease in the number of secrets. Read the documentation on Monitoring Secrets Manager with Amazon CloudWatch for more information.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
Some other launches and news that you may have missed:

IBM signed a deal with AWS to offer its software portfolio as a service on AWS. This allows customers using AWS to access IBM software for automation, data and artificial intelligence, and security that is built on Red Hat OpenShift Service on AWS.

Podcast Charlas Técnicas de AWS – If you understand Spanish, this podcast is for you. Podcast Charlas Técnicas is one of the official AWS podcasts in Spanish. This week’s episode introduces you to Amazon DynamoDB and shares stories on how different customers use this database service. You can listen to all the episodes directly from your favorite podcast app or the podcast web page.

AWS Open Source News and Updates – Ricardo Sueiras, my colleague from the AWS Developer Relation team, runs this newsletter. It brings you all the latest open-source projects, posts, and more. Read edition #112 here.

Upcoming AWS Events
It’s AWS Summits season and here are some virtual and in-person events that might be close to you:

You can register for re:MARS to get fresh ideas on topics such as machine learning, automation, robotics, and space. The conference will be in person in Las Vegas, June 21–24.

That’s all for this week. Check back next Monday for another Week in Review!

— Marcia



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Personalize your machine translation results by using fuzzy matching with Amazon Translate

A person’s vernacular is part of the characteristics that make them unique. There are often countless different ways to express one specific idea. When a firm communicates with their customers, it’s critical that the message is delivered in a way that best represents the information they’re trying to convey. This becomes even more important when…

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A person’s vernacular is part of the characteristics that make them unique. There are often countless different ways to express one specific idea. When a firm communicates with their customers, it’s critical that the message is delivered in a way that best represents the information they’re trying to convey. This becomes even more important when it comes to professional language translation. Customers of translation systems and services expect accurate and highly customized outputs. To achieve this, they often reuse previous translation outputs—called translation memory (TM)—and compare them to new input text. In computer-assisted translation, this technique is known as fuzzy matching. The primary function of fuzzy matching is to assist the translator by speeding up the translation process. When an exact match can’t be found in the TM database for the text being translated, translation management systems (TMSs) often have the option to search for a match that is less than exact. Potential matches are provided to the translator as additional input for final translation. Translators who enhance their workflow with machine translation capabilities such as Amazon Translate often expect fuzzy matching data to be used as part of the automated translation solution.

In this post, you learn how to customize output from Amazon Translate according to translation memory fuzzy match quality scores.

Translation Quality Match

The XML Localization Interchange File Format (XLIFF) standard is often used as a data exchange format between TMSs and Amazon Translate. XLIFF files produced by TMSs include source and target text data along with match quality scores based on the available TM. These scores—usually expressed as a percentage—indicate how close the translation memory is to the text being translated.

Some customers with very strict requirements only want machine translation to be used when match quality scores are below a certain threshold. Beyond this threshold, they expect their own translation memory to take precedence. Translators often need to apply these preferences manually either within their TMS or by altering the text data. This flow is illustrated in the following diagram. The machine translation system processes the translation data—text and fuzzy match scores— which is then reviewed and manually edited by translators, based on their desired quality thresholds. Applying thresholds as part of the machine translation step allows you to remove these manual steps, which improves efficiency and optimizes cost.

Machine Translation Review Flow

Figure 1: Machine Translation Review Flow

The solution presented in this post allows you to enforce rules based on match quality score thresholds to drive whether a given input text should be machine translated by Amazon Translate or not. When not machine translated, the resulting text is left to the discretion of the translators reviewing the final output.

Solution Architecture

The solution architecture illustrated in Figure 2 leverages the following services:

  • Amazon Simple Storage Service – Amazon S3 buckets contain the following content:
    • Fuzzy match threshold configuration files
    • Source text to be translated
    • Amazon Translate input and output data locations
  • AWS Systems Manager – We use Parameter Store parameters to store match quality threshold configuration values
  • AWS Lambda – We use two Lambda functions:
    • One function preprocesses the quality match threshold configuration files and persists the data into Parameter Store
    • One function automatically creates the asynchronous translation jobs
  • Amazon Simple Queue Service – An Amazon SQS queue triggers the translation flow as a result of new files coming into the source bucket

Solution Architecture Diagram

Figure 2: Solution Architecture

You first set up quality thresholds for your translation jobs by editing a configuration file and uploading it into the fuzzy match threshold configuration S3 bucket. The following is a sample configuration in CSV format. We chose CSV for simplicity, although you can use any format. Each line represents a threshold to be applied to either a specific translation job or as a default value to any job.

default, 75 SourceMT-Test, 80

The specifications of the configuration file are as follows:

  • Column 1 should be populated with the name of the XLIFF file—without extension—provided to the Amazon Translate job as input data.
  • Column 2 should be populated with the quality match percentage threshold. For any score below this value, machine translation is used.
  • For all XLIFF files whose name doesn’t match any name listed in the configuration file, the default threshold is used—the line with the keyword default set in Column 1.

Auto-generated parameter in Systems Manager Parameter Store

Figure 3: Auto-generated parameter in Systems Manager Parameter Store

When a new file is uploaded, Amazon S3 triggers the Lambda function in charge of processing the parameters. This function reads and stores the threshold parameters into Parameter Store for future usage. Using Parameter Store avoids performing redundant Amazon S3 GET requests each time a new translation job is initiated. The sample configuration file produces the parameter tags shown in the following screenshot.

The job initialization Lambda function uses these parameters to preprocess the data prior to invoking Amazon Translate. We use an English-to-Spanish translation XLIFF input file, as shown in the following code. It contains the initial text to be translated, broken down into what is referred to as segments, represented in the source tags.

Consent Form CONSENT FORM FORMULARIO DE CONSENTIMIENTO Screening Visit: Screening Visit Selección

The source text has been pre-matched with the translation memory beforehand. The data contains potential translation alternatives—represented as tags—alongside a match quality attribute, expressed as a percentage. The business rule is as follows:

  • Segments received with alternative translations and a match quality below the threshold are untouched or empty. This signals to Amazon Translate that they must be translated.
  • Segments received with alternative translations with a match quality above the threshold are pre-populated with the suggested target text. Amazon Translate skips those segments.

Let’s assume the quality match threshold configured for this job is 80%. The first segment with 99% match quality isn’t machine translated, whereas the second segment is, because its match quality is below the defined threshold. In this configuration, Amazon Translate produces the following output:

Consent Form FORMULARIO DE CONSENTIMIENTO CONSENT FORM FORMULARIO DE CONSENTIMIENTO Screening Visit: Visita de selección Screening Visit Selección

In the second segment, Amazon Translate overwrites the target text initially suggested (Selección) with a higher quality translation: Visita de selección.

One possible extension to this use case could be to reuse the translated output and create our own translation memory. Amazon Translate supports customization of machine translation using translation memory thanks to the parallel data feature. Text segments previously machine translated due to their initial low-quality score could then be reused in new translation projects.

In the following sections, we walk you through the process of deploying and testing this solution. You use AWS CloudFormation scripts and data samples to launch an asynchronous translation job personalized with a configurable quality match threshold.

Prerequisites

For this walkthrough, you must have an AWS account. If you don’t have an account yet, you can create and activate one.

Launch AWS CloudFormation stack

  1. Choose Launch Stack:
  2. For Stack name, enter a name.
  3. For ConfigBucketName, enter the S3 bucket containing the threshold configuration files.
  4. For ParameterStoreRoot, enter the root path of the parameters created by the parameters processing Lambda function.
  5. For QueueName, enter the SQS queue that you create to post new file notifications from the source bucket to the job initialization Lambda function. This is the function that reads the configuration file.
  6. For SourceBucketName, enter the S3 bucket containing the XLIFF files to be translated. If you prefer to use a preexisting bucket, you need to change the value of the CreateSourceBucket parameter to No.
  7. For WorkingBucketName, enter the S3 bucket Amazon Translate uses for input and output data.
  8. Choose Next.

    Figure 4: CloudFormation stack details

  9. Optionally on the Stack Options page, add key names and values for the tags you may want to assign to the resources about to be created.
  10. Choose Next.
  11. On the Review page, select I acknowledge that this template might cause AWS CloudFormation to create IAM resources.
  12. Review the other settings, then choose Create stack.

AWS CloudFormation takes several minutes to create the resources on your behalf. You can watch the progress on the Events tab on the AWS CloudFormation console. When the stack has been created, you can see a CREATE_COMPLETE message in the Status column on the Overview tab.

Test the solution

Let’s go through a simple example.

  1. Download the following sample data.
  2. Unzip the content.

There should be two files: an .xlf file in XLIFF format, and a threshold configuration file with .cfg as the extension. The following is an excerpt of the XLIFF file.

English to French sample file extract

Figure 5: English to French sample file extract

  1. On the Amazon S3 console, upload the quality threshold configuration file into the configuration bucket you specified earlier.

The value set for test_En_to_Fr is 75%. You should be able to see the parameters on the Systems Manager console in the Parameter Store section.

  1. Still on the Amazon S3 console, upload the .xlf file into the S3 bucket you configured as source. Make sure the file is under a folder named translate (for example, /translate/test_En_to_Fr.xlf).

This starts the translation flow.

  1. Open the Amazon Translate console.

A new job should appear with a status of In Progress.

Auto-generated parameter in Systems Manager Parameter Store

Figure 6: In progress translation jobs on Amazon Translate console

  1. Once the job is complete, click into the job’s link and consult the output. All segments should have been translated.

All segments should have been translated. In the translated XLIFF file, look for segments with additional attributes named lscustom:match-quality, as shown in the following screenshot. These custom attributes identify segments where suggested translation was retained based on score.

Custom attributes identifying segments where suggested translation was retained based on score

Figure 7: Custom attributes identifying segments where suggested translation was retained based on score

These were derived from the translation memory according to the quality threshold. All other segments were machine translated.

You have now deployed and tested an automated asynchronous translation job assistant that enforces configurable translation memory match quality thresholds. Great job!

Cleanup

If you deployed the solution into your account, don’t forget to delete the CloudFormation stack to avoid any unexpected cost. You need to empty the S3 buckets manually beforehand.

Conclusion

In this post, you learned how to customize your Amazon Translate translation jobs based on standard XLIFF fuzzy matching quality metrics. With this solution, you can greatly reduce the manual labor involved in reviewing machine translated text while also optimizing your usage of Amazon Translate. You can also extend the solution with data ingestion automation and workflow orchestration capabilities, as described in Speed Up Translation Jobs with a Fully Automated Translation System Assistant.

About the Authors

Narcisse Zekpa is a Solutions Architect based in Boston. He helps customers in the Northeast U.S. accelerate their adoption of the AWS Cloud, by providing architectural guidelines, design innovative, and scalable solutions. When Narcisse is not building, he enjoys spending time with his family, traveling, cooking, and playing basketball.

Dimitri Restaino is a Solutions Architect at AWS, based out of Brooklyn, New York. He works primarily with Healthcare and Financial Services companies in the North East, helping to design innovative and creative solutions to best serve their customers. Coming from a software development background, he is excited by the new possibilities that serverless technology can bring to the world. Outside of work, he loves to hike and explore the NYC food scene.



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Enhance the caller experience with hints in Amazon Lex

We understand speech input better if we have some background on the topic of conversation. Consider a customer service agent at an auto parts wholesaler helping with orders. If the agent knows that the customer is looking for tires, they’re more likely to recognize responses (for example, “Michelin”) on the phone. Agents often pick up…

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We understand speech input better if we have some background on the topic of conversation. Consider a customer service agent at an auto parts wholesaler helping with orders. If the agent knows that the customer is looking for tires, they’re more likely to recognize responses (for example, “Michelin”) on the phone. Agents often pick up such clues or hints based on their domain knowledge and access to business intelligence dashboards. Amazon Lex now supports a hints capability to enhance the recognition of relevant phrases in a conversation. You can programmatically provide phrases as hints during a live interaction to influence the transcription of spoken input. Better recognition drives efficient conversations, reduces agent handling time, and ultimately increases customer satisfaction.

In this post, we review the runtime hints capability and use it to implement verification of callers based on their mother’s maiden name.

Overview of the runtime hints capability

You can provide a list of phrases or words to help your bot with the transcription of speech input. You can use these hints with built-in slot types such as first and last names, street names, city, state, and country. You can also configure these for your custom slot types.

You can use the capability to transcribe names that may be difficult to pronounce or understand. For example, in the following sample conversation, we use it to transcribe the name “Loreck.”

Conversation 1

IVR: Welcome to ACME bank. How can I help you today?

Caller: I want to check my account balance.

IVR: Sure. Which account should I pull up?

Caller: Checking

IVR: What is the account number?

Caller: 1111 2222 3333 4444

IVR: For verification purposes, what is your mother’s maiden name?

Caller: Loreck

IVR: Thank you. The balance on your checking account is 123 dollars.

Words provided as hints are preferred over other similar words. For example, in the second sample conversation, the runtime hint (“Smythe”) is selected over a more common transcription (“Smith”).

Conversation 2

IVR: Welcome to ACME bank. How can I help you today?

Caller: I want to check my account balance.

IVR: Sure. Which account should I pull up?

Caller: Checking

IVR: What is the account number?

Caller: 5555 6666 7777 8888

IVR: For verification purposes, what is your mother’s maiden name?

Caller: Smythe

IVR: Thank you. The balance on your checking account is 456 dollars.

If the name doesn’t match the runtime hint, you can fail the verification and route the call to an agent.

Conversation 3

IVR: Welcome to ACME bank. How can I help you today?

Caller: I want to check my account balance.

IVR: Sure. Which account should I pull up?

Caller: Savings

IVR: What is the account number?

Caller: 5555 6666 7777 8888

IVR: For verification purposes, what is your mother’s maiden name?

Caller: Jane

IVR: There is an issue with your account. For support, you will be forwarded to an agent.

Solution overview

Let’s review the overall architecture for the solution (see the following diagram):

  • We use an Amazon Lex bot integrated with an Amazon Connect contact flow to deliver the conversational experience.
  • We use a dialog codehook in the Amazon Lex bot to invoke an AWS Lambda function that provides the runtime hint at the previous turn of the conversation.
  • For the purposes of this post, the mother’s maiden name data used for authentication is stored in an Amazon DynamoDB table.
  • After the caller is authenticated, the control is passed to the bot to perform transactions (for example, check balance)

In addition to the Lambda function, you can also send runtime hints to Amazon Lex V2 using the PutSession, RecognizeText, RecognizeUtterance, or StartConversation operations. The runtime hints can be set at any point in the conversation and are persisted at every turn until cleared.

Deploy the sample Amazon Lex bot

To create the sample bot and configure the runtime phrase hints, perform the following steps. This creates an Amazon Lex bot called BankingBot, and one slot type (accountNumber).

  1. Download the Amazon Lex bot.
  2. On the Amazon Lex console, choose Actions, Import.
  3. Choose the file BankingBot.zip that you downloaded, and choose Import.
  4. Choose the bot BankingBot on the Amazon Lex console.
  5. Choose the language English (GB).
  6. Choose Build.
  7. Download the supporting Lambda code.
  8. On the Lambda console, create a new function and select Author from scratch.
  9. For Function name, enter BankingBotEnglish.
  10. For Runtime, choose Python 3.8.
  11. Choose Create function.
  12. In the Code source section, open lambda_function.py and delete the existing code.
  13. Download the function code and open it in a text editor.
  14. Copy the code and enter it into the empty function code field.
  15. Choose deploy.
  16. On the Amazon Lex console, select the bot BankingBot.
  17. Choose Deployment and then Aliases, then choose the alias TestBotAlias.
  18. On the Aliases page, choose Languages and choose English (GB).
  19. For Source, select the bot BankingBotEnglish.
  20. For Lambda version or alias, enter $LATEST.
  21. On the DynamoDB console, choose Create table.
  22. Provide the name as customerDatabase.
  23. Provide the partition key as accountNumber.
  24. Add an item with accountNumber: “1111222233334444” and mothersMaidenName “Loreck”.
  25. Add item with accountNumber: “5555666677778888” and mothersMaidenName “Smythe”.
  26. Make sure the Lambda function has permissions to read from the DynamoDB table customerDatabase.
  27. On the Amazon Connect console, choose Contact flows.
  28. In the Amazon Lex section, select your Amazon Lex bot and make it available for use in the Amazon Connect contact flow.
  29. Download the contact flow to integrate with the Amazon Lex bot.
  30. Choose the contact flow to load it into the application.
  31. Make sure the right bot is configured in the “Get Customer Input” block.
  32. Choose a queue in the “Set working queue” block.
  33. Add a phone number to the contact flow.
  34. Test the IVR flow by calling in to the phone number.

Test the solution

You can now call in to the Amazon Connect phone number and interact with the bot.

Conclusion

Runtime hints allow you to influence the transcription of words or phrases dynamically in the conversation. You can use business logic to identify the hints as the conversation evolves. Better recognition of the user input allows you to deliver an enhanced experience. You can configure runtime hints via the Lex V2 SDK. The capability is available in all AWS Regions where Amazon Lex operates in the English (Australia), English (UK), and English (US) locales.

To learn more, refer to runtime hints.

About the Authors

Kai Loreck is a professional services Amazon Connect consultant. He works on designing and implementing scalable customer experience solutions. In his spare time, he can be found playing sports, snowboarding, or hiking in the mountains.

Anubhav Mishra is a Product Manager with AWS. He spends his time understanding customers and designing product experiences to address their business challenges.

Sravan Bodapati is an Applied Science Manager at AWS Lex. He focuses on building cutting edge Artificial Intelligence and Machine Learning solutions for AWS customers in ASR and NLP space. In his spare time, he enjoys hiking, learning economics, watching TV shows and spending time with his family.



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