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RAG for Diagnostic Reasoning
Healthcare AI

RAG for Diagnostic Reasoning

Clinical Notes Reasoning System using MIMIC-IV-Ext-Direct

RAGFAISSFlan-T5GradioMIMIC-IV

Problem Statement

Extracting precise clinical diagnostic signals from unstructured hospital electronic health records (EHR).

Proposed Solution

Engineered a Retrieval-Augmented Generation (RAG) pipeline coupling FAISS vector index with Google Flan-T5 model.

Key Features

  • Vector indexing of MIMIC-IV clinical notes with FAISS
  • Flan-T5 LLM context-conditioned diagnostic generation
  • Colab Notebook & Gradio interface deployment
  • Hallucination reduction through strict medical context grounding

System Architecture Flow

Clinical Query -> FAISS Similarity Search -> Relevant Notes -> Flan-T5 LLM -> Diagnosis

Engineering Challenges

Handling noisy medical abbreviations and protecting sensitive patient EHR schemas.

Lessons Learned

Mastering RAG chunking strategies, vector similarity metrics, and LLM prompt engineering.