Abstract
Much clinically salient information in Japanese electronic medical records is recorded in SOAP (Subjective, Objective, Assessment, Plan) notes, which contain a lot of unstructured text preventing the further usage for analysis. Structuring such unstructured text is essential for secondary data use and clinical research. This study aimed to develop a schema-guided, hierarchical multi-label (15 first-level and 58 second-level labels) framework for structuring respiratory medicine SOAP notes using a locally deployed large language model (LLaMA-3.1-8B adapted with QLoRA). 200 randomly selected notes were annotated and split in two for training and test. Performance was assessed through atomic-cell matching under both strict and tolerant similarity metrics, achieved a micro-averaged F1 of 0.49 (strict) and 0.59 (Levenshtein> 0.7). Common and standardized fields such as vital signs were reliably structured, while ambiguous boundaries and rare labels remained challenging. These findings demonstrate that schema-guided hierarchical multi-label frameworks can support the structuring of Japanese clinical notes and serve as a foundation for downstream research and quality-improvement workflows.