文献类型：期刊文献

英文题名：EMRs2CSP: Mining Clinical Status Pathway from Electronic Medical Records

作者：Chen, Yifei[1]; Hou, Ruihui[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：17235

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

收录：EI(收录号：20260519990654)

语种：英文

外文关键词：Data mining - Diagnosis - Electronic health record - Medical computing

摘要：Many current studies focus on extracting tests or treatments when constructing clinical pathways, often neglecting the patient's symptoms and diagnosis, leading to incomplete diagnostic and therapeutic logic. Therefore, this paper aims to extract clinical pathways from electronic medical records that encompass complete diagnostic and therapeutic logic, including temporal information, patient symptoms, diagnosis, and tests or treatments. To achieve this objective, we propose a novel clinical pathway representation: the clinical status pathway. We also design a LLM-based pipeline framework for extracting clinical status pathway from electronic medical records, with the core concept being to improve extraction accuracy by modeling the diagnostic and treatment processes. In our experiments, we apply this framework to construct a comprehensive breast cancer-specific clinical status pathway and evaluate its performance on medical question-answering and decision-support tasks, demonstrating significant improvements over traditional clinical pathways. The code is publicly available at https://github.com/finnchen11/EMRs2CSP. ? 2025 Association for Computational Linguistics.