A patient-centered approach to developing and validating a natural language processing model for extracting patient-reported symptoms.

ABSTRACT: Patient-reported symptoms provide valuable insights into patient experiences and can enhance healthcare quality; however, effectively capturing them remains challenging. Although natural language processing (NLP) models have been developed to extract adverse events and symptoms from medical records written by healthcare professionals, limited studies have focused on models designed for patient-generated narratives. This study developed an NLP model to extract patient-reported symptoms from pharmaceutical care records and validated its effectiveness in analyzing diverse patient-generated narratives. The target dataset comprised "Subjective" sections of pharmaceutical care records created by community pharmacists for patients prescribed anticancer drugs. Two annotation guidelines were applie

SUBMITTER: Watabe S 

PROVIDER: S-EPMC12307582 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature