Talk to Your Medical History: Building a Personal EHR RAG with Milvus and Unstructured.io 🩺
We’ve all been there: digging through a mountain of crumpled hospital printouts, blurry scans, and nested PDFs just to find out what that specific blood test result was three years ago. Medical data is messy, unstructured, and—let's be honest—doctor's handwriting is the final boss of OCR. In this tutorial, we are building a Personal Electronic Health Record (EHR) RAG system . We will transform…
The article outlines a method for creating a Personal Electronic Health Record (EHR) Retrieval-Augmented Generation (RAG) system using Unstructured.io, BGE embeddings, and Milvus vector database. The system aims to transform chaotic medical PDFs and scans into a searchable, intelligent knowledge base. The process involves three main steps: shredding the data with Unstructured.io, embedding the data with BGE, and storing the embeddings in Milvus.
The system can then answer complex queries about the user's medical history by retrieving relevant information from the vector database. The article also discusses the challenges of working with medical documents, such as complex layouts, scanned images, and semantic context. It highlights the importance of a vector database like Milvus for storing high-dimensional data and its ability to handle metadata filtering and persistence.
The article concludes by encouraging readers to explore more production-ready examples and consider adding a Reranker for improved accuracy and implementing a frontend using Streamlit for easier data uploads.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
