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.

随着生成式大语言模型（Large Language Model，LLM）在自然语言处理领域的飞速发展，其在医疗应用中的潜力愈发凸显。然而，现有多数研究多依赖考试式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数据库构建了其子集MIMIC-IV-Ext 心脏疾病数据集（MIMIC-IV-Ext Cardiac Disease），共纳入4761名确诊心脏疾病的患者。该数据集涵盖患者自入院至出院全流程的相关临床检查信息与最终诊断结论。将该数据集与我们搭建的多轮交互框架相结合，可用于评估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。