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