CHEN Yucong1, TAN Weifeng2,*, RONG Weixin1, HAN Chunchun1, WANG Gengbin1

Corresponding author: tanweifeng, tanweifeng@jmszxyy.com.cn

DOI: 10.12201/bmr.202510.00038

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  Abstract: 【Abstract】 Objective/Significance: To establish a medical record generation agent with a large language model as the core to realize the intelligent generation of electronic medical record instruments and reduce the pressure of clinical medical record writing. Method/Process: Through data cleaning and locally deployed large language model simulation for dialogue generation, the data reserve for model fine-tuning is completed, and model fine-tuning technology is utilized to train the general large language model into a professional model for medical record generation, meanwhile, a local knowledge base is established based on standards such as medical record writing specifications, and finally, the medical record generating agent is built with the fine-tuned model. Results/Conclusions: The evaluation results by model fine-tuning show that the medical record instruments generated by the large language model after supervised fine-tuning are more accurate and more in line with the requirements of medical record instrument specifications. By designing the workflow of intelligibles with retrieval of local knowledge base retrieval correction, it can make the generated medical record text more in line with the requirements of medical quality control. This study combines multiple technologies to construct a medical record generating agent, and the generated electronic medical record documents have higher professionalism and reliability, which provides empirical reference and empirical support for the application and development of intelligent medical record generation.

  Key words: Medical; record generation, Large; language model, Agent, Model; fine-tuning, Knowledge; base

  Submit time: 22 October 2025

  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	2025-08-19	10.12201/bmr.202510.00038V1	Download

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