Medical question answering method and system based on multi-stage preference alignment retrieval enhancement

Abstract

The application provides a medical question and answer method and system based on multi-stage preference alignment retrieval enhancement, and belongs to the technical field of natural language processing, and comprises the following steps: performing multi-pass semantic expansion on input medical problems and patient case information, integrating query expressions generated by different paths to obtain a multi-pass input set, inputting the multi-pass input set into a retrieval model to obtain a retrieval evidence set; based on the retrieval evidence set, the medical problems and the patient case information, introducing a preset prompt strategy, using a large language model to analyze the characteristics of the case information to obtain a clinical feature set and iteratively updating the clinical feature set to obtain a retrieval candidate evidence set; screening supporting documents based on the retrieval candidate evidence set; and inputting the supporting documents, the medical problems and the patient medical record information into a model optimized through multi-stage preference learning to generate corresponding medical reasoning processes and question and answer results. The application improves the overall stability and accuracy of the medical question and answer system.

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