Automated Clinical Note Section Identification Using Transfer Learning and Contextual Embeddings

The lack of structured formatting in clinical notes, often generated during patient consultations, contradicts established clinical practice guidelines advocating for structured formats such as SOAP. Building upon previous clinical note section identification research, this study employs Transfer Learning models integrated with clinical contextual embeddings for the automated classification of clinical notes into distinct major SOAP sections. Validated on a specialized dataset focusing on the cardiology department, the study substantiates the feasibility of developing intelligent note-taking applications using Transfer Learning. The potential for integration into Hospital Information Systems is highlighted, promising to streamline clinical note composition and address the issue of physician burnout.