MedDec: A Dataset for Extracting Medical Decisions from Discharge Summaries
This work addresses the need for structured data to analyze medical decision-making in clinical settings, but it is incremental as it focuses on dataset creation and baseline evaluation without major methodological breakthroughs.
The authors tackled the problem of extracting medical decisions from clinical notes by creating the MedDec dataset with annotations for ten types of decisions across eleven diseases, and they developed a baseline span detection model to evaluate the task's complexity.
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.