MedDecXtract: A Clinician-Support System for Extracting, Visualizing, and Annotating Medical Decisions in Clinical Narratives

Clinical notes contain crucial information about medical decisions such as treatments, diagnoses and follow-ups. However, these decisions are embedded within unstructured text, making it challenging to computationally analyze clinical decision-making patterns or support clinical workflows. We present MedDecXtract: an open-source and interactive system that automatically extracts and visualizes medical decisions from clinical text. The system implements a RoBERTa-based model for identifying ten categories of medical decisions (e.g., diagnosis, treatment, follow-up) according to the Decision Identification and Classification Taxonomy for Use in Medicine (DICTUM), and provides an intuitive interface for exploration, visualization, and annotation. MedDecXtract and its source code can be accessed at https: //mohdelgaar-clinical-decisions.hf . space. A video demo is available at https://youtu.be/19j6-XtIE_s .