Analyzing Health Data with AI: A Hybrid Approach

The use of Large Language Models (LLMs) in the healthcare sector offers enormous potential for processing clinical data. However, limitations regarding context grounding and hallucinations pose significant challenges. A new approach, called MediGRAF (Medical Graph Retrieval Augmented Framework), addresses these issues by combining the capabilities of Neo4j Text2Cypher for structured relationship analysis with vector embeddings for unstructured information retrieval.

MediGRAF: A Framework for Querying Clinical Data

MediGRAF enables natural language querying of the complete patient journey, integrating data from various sources. In an evaluation on 10 patients using the MIMIC-IV dataset (with 5,973 nodes and 5,963 relationships), the system demonstrated 100% recall for factual questions. Furthermore, it achieved an average quality score of 4.25/5 in complex inference tasks, without compromising data security.

Implications for the Future of Clinical AI

These results suggest that MediGRAF's hybrid approach represents a significant advance in clinical information retrieval, offering a safer and more comprehensive alternative to standard LLM deployments. This type of solution could foster a better understanding of clinical data and more effective decision support for physicians.