Machine learning in psychiatric health records: A gold standard approach to trauma annotation.

ABSTRACT: Psychiatric electronic health records present unique challenges for machine learning due to their unstructured, complex, and variable nature. This study aimed to create a gold standard dataset by identifying a cohort of patients with psychotic disorders and posttraumatic stress disorder, (PTSD), developing clinically-informed guidelines for annotating traumatic events in their health records and to create a gold standard publicly available dataset, and demonstrating the dataset's suitability for training machine learning models to detect indicators of symptoms, substance use, and trauma in new records. We compiled a representative corpus of 200 narrative heavy health records (470,489 tokens) from a centralized database and developed a detailed annotation scheme with a team of clinical expe

SUBMITTER: Holderness E 

PROVIDER: S-EPMC12317041 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature