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. In this paper, we develop a new dataset called "MedDec", which contains clinical notes of eleven different phenotypes (diseases) annotated by ten types of medical decisions. We introduce the task of medical decision extraction, aiming to jointly extract and classify different types of medical decisions within clinical notes. We provide a comprehensive analysis of the dataset, develop a span detection model as a baseline for this task, evaluate recent span detection approaches, and employ a few metrics to measure the complexity of data samples. Our findings shed light on the complexities inherent in clinical decision extraction and enable future work in this area of research. The dataset and code are available through https://github.com/CLU-UML/MedDec.

翻译：医疗决策直接影响个体的健康与福祉。从临床记录中提取决策片段对于理解医疗决策过程具有关键作用。本文开发了一个名为“MedDec”的新数据集，其中包含十一种不同表型（疾病）的临床记录，并由十类医疗决策进行了标注。我们提出了医疗决策提取任务，旨在从临床记录中联合提取并分类不同类型的医疗决策。我们对数据集进行了全面分析，构建了作为任务基线的片段检测模型，评估了最新的片段检测方法，并采用多种指标衡量数据样本的复杂性。我们的研究揭示了临床决策提取中固有的复杂性，并为该研究领域的未来工作奠定了基础。数据集与代码可通过 https://github.com/CLU-UML/MedDec 获取。