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ACL 2024aclfindings

MedDec: A Dataset for Extracting Medical Decisions from Discharge Summaries

Mohamed Elgaar, Jiali Cheng, Nidhi Vakil, Hadi Amiri, Leo Anthony Celi

University of Massachusetts, Lowell · University of Massachusetts at Lowell · University of Massachusetts Lowell · Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center

PDF 由论文原始站点提供,PaperCompass 不保存论文文件。DOI 10.18653/v1/2024.findings-acl.975 ↗

摘要

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.