Medical Retrieval-Augmentation Generation Framework for Healthcare Prediction

Area of Science:

• Medical Informatics
• Artificial Intelligence in Healthcare
• Clinical Data Science

Background:

• Electronic Health Records (EHRs) are crucial for healthcare prediction, containing extensive patient data.
• Current Retrieval-Augmented Generation (RAG) methods for medical applications often use flat data, failing to capture intricate medical entity relationships and leading to suboptimal predictions.
• This limitation results in fragmented and verbose outputs, hindering effective healthcare predictions.

Purpose of the Study:

• To propose MedGR, a novel framework for healthcare prediction that enhances EHR data representation.
• To address the limitations of flat data structures in existing RAG approaches for medical applications.
• To improve the coherence, contextual enrichment, and efficiency of responses in medical predictions.

Main Methods:

• Developed MedGR, a framework integrating graph-based clinical text indexing with a dual-level medical retrieval architecture.
• Utilized graph-structured knowledge to synthesize information from multiple sources.
• Implemented a novel approach to capture complex inter-dependencies among medical entities within EHR data.

Main Results:

• The MedGR framework demonstrated high precision in both diagnosis prediction and medical code prediction tasks.
• Graph-based indexing and dual-level retrieval effectively synthesized information, leading to coherent and contextually enriched responses.
• The proposed medical RAG framework showed significant improvements over existing methods in handling complex medical data.

Conclusions:

• MedGR offers an efficient and effective solution for healthcare prediction tasks by leveraging graph-structured knowledge.
• The framework successfully overcomes the limitations of flat data representations in EHRs for RAG applications.
• MedGR provides a promising advancement in improving the accuracy and quality of predictions derived from Electronic Health Records.