Intelligent Q&A Study of Traditional Chinese Medicine for Parkinsons Disease Based on Knowledge Graph and Large Language Modeling

Corresponding author: Liuxiufeng, liu_xf@gzucm.edu.cn

DOI: 10.12201/bmr.202604.00033

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  Abstract: This study aims to build an intelligent question-answering system based on Traditional Chinese Medicine (TCM) using knowledge graphs and large language models (LLMs), and to achieve the visualization and data management of TCM treatments for Parkinsons disease. First, by collecting and processing TCM medical case data for Parkinsons disease, deep learning techniques were used to extract information, which was then structured and stored in a Neo4j graph database. The knowledge graph encompasses information such as syndrome types, prescriptions, Chinese herbs, and symptoms, providing a rich source of structured data for the question-answering system. Next, the P-Tuning v2 method was employed to fine-tune the large language model, integrating the knowledge graph to optimize the models question-answering performance. Experimental results show that, compared to the baseline model without fine-tuning, the fine-tuned model significantly improves in terms of accuracy, fluency, and coverage of information. This study proposes an intelligent question-answering system that offers users more personalized and intelligent TCM treatment recommendations, contributing to the promotion of TCM therapies for Parkinson’s disease.

  Key words: TCM medical records; Large language model; Parkinsons disease; Knowledge graph

  Submit time: 7 April 2026

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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  1	2025-12-19	10.12201/bmr.202604.00033V1	Download

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