MedDec: Medical Decisions for Discharge Summaries in the MIMIC-III Database

Medical decisions directly impact individuals' health and well-being.
Extracting decision spans from clinical notes plays a crucial role in
understanding medical decision-making processes. The MedDec dataset includes
expert-annotated decisions of ten types, including defining problems, setting
treatment goals, making drug-related decisions, performing therapeutic
procedures, giving advice and precautions, and deferment decisions, spanning
across eleven diverse patient phenotypes such as heart disease, lung disease,
cancer, psychiatric disorders, and chronic pain, from 451 expert-annotated
discharge summaries in the MIMIC-III database, where annotators identified
decision spans within each discharge summary, noting character start and end
positions alongside the decision type. The dataset enables the task of medical
decision extraction, aiming to jointly extract and classify different types of
medical decisions within clinical notes. This dataset also provides insights
into medical decisions across diverse demographics, including gender, race,
and English proficiency, sourced from the MIMIC-III database

医疗决策直接影响个体的健康与福祉。从临床笔记（clinical note）中抽取决策片段（decision span），对于理解医疗决策制定过程具有关键意义。MedDec数据集包含专家标注的十种类型决策，涵盖问题界定、治疗目标设定、药物相关决策、治疗操作执行、健康建议与预防措施给出以及延期决策；该数据集源自MIMIC-III数据库中的451份专家标注出院小结，覆盖11种不同的患者表型，包括心脏病、肺部疾病、癌症、精神障碍与慢性疼痛等。数据集的标注人员会在每份出院小结中识别决策片段，同时标注其字符起始与结束位置以及决策类型。本数据集支持医疗决策抽取任务，旨在从临床笔记中联合抽取并分类不同类型的医疗决策。此外，本数据集还提供了来自MIMIC-III数据库、覆盖性别、种族与英语水平等多元人口统计学特征的医疗决策相关研究洞察。