Healthcare Information Extraction Using Amazon Bedrock using advanced NLP with Titan or Claude Models

Healthcare Information Extraction Using Amazon Bedrock

Client: Leading Healthcare Provider

Project Overview:
This project was developed for a healthcare client to automate the extraction of critical patient information from unstructured medical records using advanced Natural Language Processing (NLP) capabilities offered by Amazon Bedrock. The primary objective was to streamline the processing of patient case narratives, reducing the manual effort needed to identify key data points such as patient demographics, symptoms, medical history, medications, and recommended treatments.

Key Features Implemented:

1. Automated Text Analysis: Utilized Amazon Bedrock's NLP models to analyze healthcare use cases, automatically identifying and extracting relevant clinical details.
2. Customizable Information Extraction: Implemented the solution to support specific healthcare entities (e.g., patient name, age, symptoms, medications) using customizable extraction models.
3. Seamless Integration: Integrated with existing systems using Java-based AWS SDK, enabling the healthcare provider to leverage the extracted information for clinical decision support and reporting.
4. Real-time Data Processing: Enabled the client to process patient case records in real-time, accelerating the review of patient documentation and improving overall efficiency.

Amazon Bedrock provides access to foundational models for Natural Language Processing (NLP), which can be used for various applications, such as extracting relevant information from text documents. Below is the implementation design with Amazon Bedrock with Java to analyze patient healthcare use cases. For this example, I will illustrate how to structure a solution that utilizes AWS SDK for Java to interact with Bedrock and apply language models like Titan or Claude (depending on the model availability).

Prerequisites

1. AWS SDK for Java: Make sure you have included the necessary dependencies for interacting with Amazon Bedrock.
2. Amazon Bedrock Access: Ensure that your AWS credentials and permissions are configured to access Amazon Bedrock.
3. Java 11 or Higher: Recommended to use a supported version of Java.

Step 1: Include Maven Dependencies

First, add the necessary dependencies in your pom.xml to include the AWS SDK for Amazon Bedrock.

xml

<dependency>
    <groupId>software.amazon.awssdk</groupId>
    <artifactId>bedrock</artifactId>
    <version>2.20.0</version>
</dependency>

Step 2: Set Up AWS SDK Client

Next, create a client to connect to Amazon Bedrock using the BedrockClient provided by the AWS SDK.

java code
import software.amazon.awssdk.auth.credentials.ProfileCredentialsProvider;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrock.BedrockClient;
import software.amazon.awssdk.services.bedrock.model.*;

public class BedrockHelper {

    public static BedrockClient createBedrockClient() {
        return BedrockClient.builder()
                .region(Region.US_EAST_1)  // Set your AWS region
                .credentialsProvider(ProfileCredentialsProvider.create())
                .build();
    }
}

Step 3: Define a Method to Extract Information

Create a method that will interact with Amazon Bedrock, pass the healthcare use case text, and get relevant information back.

java

import software.amazon.awssdk.services.bedrock.model.InvokeModelRequest;
import software.amazon.awssdk.services.bedrock.model.InvokeModelResponse;

public class HealthcareUseCaseProcessor {

    private BedrockClient bedrockClient;

    public HealthcareUseCaseProcessor(BedrockClient bedrockClient) {
        this.bedrockClient = bedrockClient;
    }

    public String extractRelevantInformation(String useCaseText) {
        InvokeModelRequest request = InvokeModelRequest.builder()
                .modelId("titan-chat-b7") // Replace with the relevant model ID
                .body("{ \"text\": \"" + useCaseText + "\" }")
                .build();

        InvokeModelResponse response = bedrockClient.invokeModel(request);
        return response.body();  // The response will contain the extracted information
    }
}

Step 4: Analyze Patient Healthcare Use Cases

This example uses a test healthcare use case to demonstrate the interaction.

java
public class BedrockApp {
    public static void main(String[] args) {
        BedrockClient bedrockClient = BedrockHelper.createBedrockClient();
        HealthcareUseCaseProcessor processor = new HealthcareUseCaseProcessor(bedrockClient);

        // Sample healthcare use case text
        String healthcareUseCase = "Patient John Doe, aged 45, reported symptoms of chest pain and dizziness. "
                + "Medical history includes hypertension and type 2 diabetes. "
                + "Prescribed medication includes Metformin and Atenolol. "
                + "Referred for an ECG and follow-up with a cardiologist.";

        // Extract relevant information
        String extractedInfo = processor.extractRelevantInformation(healthcareUseCase);

        // Print the extracted information
        System.out.println("Extracted Information: " + extractedInfo);
    }
}

Step 5: Handling the Extracted Information

The extractRelevantInformation method uses Amazon Bedrock’s language models to identify key data points. Depending on the model and the request format, you may want to parse and analyze the output JSON.

For example, if the output JSON has a specific structure, you can use libraries like Jackson or Gson to parse the data:

java
import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.ObjectMapper;

public void processResponse(String jsonResponse) {
    ObjectMapper mapper = new ObjectMapper();
    try {
        JsonNode rootNode = mapper.readTree(jsonResponse);
        JsonNode patientName = rootNode.get("patient_name");
        JsonNode age = rootNode.get("age");
        System.out.println("Patient Name: " + patientName.asText());
        System.out.println("Age: " + age.asText());
    } catch (Exception e) {
        e.printStackTrace();
    }
}

Points to Consider

1. Model Selection: Choose the correct model that suits your use case, such as those specialized in entity extraction or text classification.
2. Region Availability: Amazon Bedrock is available in specific regions. Make sure you are using the right region.
3. API Limits: Be aware of any rate limits or quotas for invoking models on Amazon Bedrock.