Clinical Decision Support System Using AWS Bedrock Agent and RAG Frontend Streamlit

Clinical Decision Support Systems (CDSS) play a vital role in enhancing healthcare by providing healthcare professionals with data-driven insights for diagnosis and treatment. By integrating AWS Bedrock Agent and a RAG (Retrieval-Augmented Generation) frontend using Streamlit, we can create an efficient and scalable CDSS.

What is AWS Bedrock Agent?

AWS Bedrock Agent is a managed service that allows developers to build and deploy machine learning models for various applications. It simplifies the integration of large language models (LLMs) and provides robust infrastructure to support AI-driven applications.

What is RAG?

Retrieval-Augmented Generation (RAG) is an approach that enhances language models by incorporating external knowledge retrieval. This method improves the accuracy and relevance of responses by fetching additional data from external sources.

Streamlit for Frontend

Streamlit is a powerful Python-based framework that allows rapid development of interactive web applications. It is especially useful for data-driven applications like a CDSS due to its simplicity and flexibility.

Architecture Overview

1. Data Ingestion: Collect patient data, clinical guidelines, and medical literature.
2. AWS Bedrock Agent Integration: Deploy the LLM on AWS Bedrock for inference.
3. RAG Mechanism: Use a retrieval system to fetch relevant clinical data.
4. Streamlit Frontend: Build an intuitive user interface for healthcare professionals.
5. Decision Support: Present recommendations and insights to assist in diagnosis and treatment planning.

Step-by-Step Implementation

Step 1: AWS Bedrock Agent Setup

• Access the AWS Management Console.
• Create an Amazon Bedrock endpoint.
• Train or fine-tune the LLM with relevant medical data.
• Deploy the model and obtain the API endpoint.

Step 2: RAG Implementation

• Use a vector database (e.g., FAISS or Elasticsearch) to index clinical guidelines and patient records.
• Implement a retrieval function to fetch relevant data based on user queries.

Step 3: Streamlit Frontend Development

Install Streamlit:

pip install streamlit

Create a Streamlit app (app.py):

import streamlit as st
import requests

st.title("Clinical Decision Support System")

query = st.text_input("Enter Symptoms or Diagnosis Query:")

if st.button("Get Recommendation"):
    response = requests.post("<AWS_BEDROCK_API_ENDPOINT>", json={"query": query})
    st.write("Recommendation:", response.json().get("response"))

Run the app:

streamlit run app.py

Step 4: Evaluation and Deployment

• Evaluate the system’s accuracy with clinical experts.
• Deploy the application on AWS EC2 or any cloud server.

Benefits of the Proposed System

• Improved diagnostic accuracy
• Real-time insights and recommendations
• Scalable and secure infrastructure

Conclusion

Integrating AWS Bedrock Agent and RAG with a Streamlit frontend provides a powerful and user-friendly Clinical Decision Support System. This approach leverages the strengths of AI-driven models and intuitive UI design to enhance healthcare outcomes.