文献类型：期刊文献

英文题名：CMQCIC-Bench: A Chinese Benchmark for Evaluating Large Language Models in Medical Quality Control Indicator Calculation

作者：Yu, Guangya[1]; Li, Yanhao[1]; Jiang, Zongying[1]; Jin, Yuxiong[1]; Dai, Li[1]; Lin, Yupian[1]; Hou, Ruihui[1]; Zhang, Weiyan[1]; Fan, Yongqi[1]; Ye, Qi[1]; Liu, Jingping[1]; Ruan, Tong[1]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China

年份：2025

起止页码：609

外文期刊名：Proceedings of the Annual Meeting of the Association for Computational Linguistics

收录：EI(收录号：20250089928)

语种：英文

外文关键词：Benchmarking - Electronic health record - Medical computing - Quality control

摘要：Medical quality control indicators are essential to assess the qualifications of healthcare institutions for medical services. With the impressive performance of large language models (LLMs) like GPT-4 in the medical field, leveraging these technologies for the Medical Quality Control Indicator Calculation (MQCIC) presents a promising approach. In this work, (1) we introduce a real-world task MQCIC and propose an open-source Chinese electronic medical records (EMRs)-based dataset (CMQCIC-Bench) comprising 785 instances and 76 indicators. (2) We propose a semiautomatic method to enhance the rule representation. Then we propose the Clinical Facts-based Inferential Rule (CF-IR) method that disentangles the clinical fact verification and inferential rule reasoning actions. (3) We conduct comprehensive experiments on 20 representative LLMs, covering general and medical models. Our findings reveal that CF-IR outperforms Chain-of-Thought methods in MQCIC tasks. (4) We conduct an error analysis and investigate the capabilities of clinical fact verification and inferential rule reasoning, providing insights to improve performance in the MQCIC further. The dataset and code is available in this repository. ? 2025 Association for Computational Linguistics.