Large language models with retrieval-augmented generation enhance expert modelling of Bayesian network for clinical decision support

Abstract

PURPOSE: Bayesian networks (BNs) are valuable for clinical decision support due to their transparency and interpretability. However, BN modelling requires considerable manual effort. This study explores how integrating large language models (LLMs) with retrieval-augmented generation (RAG) can improve BN modelling by increasing efficiency, reducing cognitive workload, and ensuring accuracy. METHODS: We developed a web-based BN modelling service that integrates an LLM-RAG pipeline. A fine-tuned GTE-Large embedding model was employed for knowledge retrieval, optimised through recursive chunking and query expansion. To ensure accurate BN suggestions, we defined a causal structure for medical idioms by unifying existing BN frameworks. GPT-4 and Mixtral 8x7B were used to handle complex data interpretation and to generate modelling suggestions, respectively. A user study with four clinicians assessed usability, retrieval accuracy, and cognitive workload using NASA-TLX. The study demonstrated the system's potential for efficient and clinically relevant BN modelling. RESULTS: 0.9). However, the LLM occasionally failed to adhere to predefined causal structures and medical idioms. All clinicians, regardless of BN experience, created comprehensive models within one hour. Experienced clinicians produced more complex models, but occasionally introduced causality errors, while less experienced users adhered more accurately to predefined structures. The tool reduced cognitive workload (2/7 NASA-TLX) and was described as intuitive, although workflow interruptions and minor technical issues highlighted areas for improvement. CONCLUSION: Integrating LLM-RAG into BN modelling enhances efficiency and accuracy. Future work may focus on automated preprocessing, refinements of the user interface, and extending the RAG pipeline with validation steps and external biomedical sources. Generative AI holds promise for expert-driven knowledge modelling.

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