Designing and Building an LLM-Powered RAG System for Clinical Data Analysis on AWS: Uncovering Root Causes and Patterns in Chronic Diseases
Overview: This thesis focuses on developing a Retrieval-Augmented Generation (RAG) system powered by Large Language Models (LLMs) and deployed on AWS cloud infrastructure. The system will analyze and synthesize information from a comprehensive collection of trusted clinical sources to identify root causes and recurring patterns in chronic diseases. Leveraging AWS services—including AWS Bedrock for generative AI, secure data storage, and scalable compute—the project will address the challenges of aggregating heterogeneous clinical data, ensuring source reliability, and extracting actionable insights to support medical research and healthcare decision-making.
Description
Key Components:
1. Clinical Data Collection and Integration on AWS
- Aggregate data from diverse, trusted clinical sources (e.g., peer-reviewed journals, medical databases, hospital records, public health datasets).
- Use AWS services such as Amazon S3 for secure storage and AWS Glue for data integration and transformation.
- Ensure data quality, reliability, and compliance with privacy regulations (e.g., HIPAA, GDPR) using AWS security and compliance tools.
2. LLM and RAG Architecture Development with AWS Bedrock
- Design and implement a RAG pipeline using state-of-the-art LLMs available through AWS Bedrock (e.g., Anthropic Claude, Amazon Titan, or third-party models).
- Integrate retrieval mechanisms using AWS OpenSearch or Amazon Kendra to access relevant clinical documents and datasets in real time.
- Fine-tune the LLM on domain-specific medical corpora to improve accuracy and relevance, leveraging AWS SageMaker for model training and evaluation.
3. Root Cause and Pattern Analysis
- Develop algorithms to identify correlations, causal relationships, and recurring patterns in chronic disease data.
- Apply statistical and machine learning methods using AWS SageMaker and related analytics services to validate findings and reduce bias.
4. Evaluation and Validation
- Benchmark the system against existing clinical analysis tools.
- Collaborate with medical experts to assess the validity and utility of generated insights.
- Conduct case studies on selected chronic diseases (e.g., diabetes, cardiovascular disease, autoimmune disorders).
Challenges:
- Data Heterogeneity: Integrating and harmonizing data from multiple clinical sources with varying formats and standards.
- Source Trustworthiness: Ensuring all data used is from verified, reputable sources to maintain scientific rigor.
- Model Bias and Explainability: Addressing potential biases in LLM outputs and providing transparent, interpretable results for clinical stakeholders.
- Privacy and Compliance: Managing sensitive patient data in accordance with legal and ethical guidelines, leveraging AWS’s compliance features.
- Scalability: Designing the system to handle large-scale data and adapt to new sources and disease domains using AWS’s scalable infrastructure.
Impact:
- For Academia: Provides a novel framework for AI-driven clinical research, enabling deeper understanding of chronic disease mechanisms and supporting future studies.
- For Healthcare Industry: Offers a powerful tool for clinicians and researchers to uncover actionable insights, improve diagnosis, and inform treatment strategies.
- For Society: Contributes to better public health outcomes by facilitating early detection, prevention, and management of chronic diseases through data-driven analysis.