MIMIC-IV-Ext Cardiac Disease

With the rapid development of generative LLMs (large language models) in the
field of natural language processing, their potential in medical applications
has become increasingly evident. However, most existing studies rely on exam-
style questions or artificially designed cases, lacking validation using real
patient data. To address this gap, this study leverages the MIMIC-IV database
to construct a subset, MIMIC-IV-Ext Cardiac Disease, which includes 4,761
patients diagnosed with cardiac diseases. The dataset covers all relevant
clinical examinations from admission to discharge, as well as the final
diagnoses.Combining these data with the multi-turn interaction framework we
built can be used to test whether large models can guide patients through in-
hospital examinations. Moreover, after modifying the MIMIC-IV dataset, our
sub-dataset can greatly facilitate researchers in conducting other studies.

随着自然语言处理领域内生成式大语言模型（Large Language Models，LLM）的快速发展，其在医疗应用领域的潜力愈发凸显。然而，当前多数相关研究均依赖于考试题型类问题或人工设计的病例，缺乏基于真实患者数据的验证。为填补这一研究空白，本研究依托MIMIC-IV数据库构建了MIMIC-IV-Ext Cardiac Disease子集，该子集包含4761名确诊心脏疾病的患者。本数据集涵盖了患者从入院至出院的全部相关临床检查信息与最终诊断结果。将这些数据与我们构建的多轮交互框架相结合，可用于测试大语言模型能否引导患者完成院内检查流程。此外，经改造后的本MIMIC-IV子数据集可极大助力研究人员开展其他相关研究工作。

搜集汇总

背景与挑战

背景概述

MIMIC-IV-Ext Cardiac Disease数据集是一个专注于心脏疾病的临床数据集，包含4,761名患者的完整临床记录，涵盖从入院到出院的所有检查数据。该数据集旨在支持真实临床环境中的大型语言模型评估和其他医学研究。