Artificial intelligence for detecting bipolar disorder in electronic health records of patients with affective diagnoses: a diagnostic accuracy study

BackgroundBipolar disorder (BD) is frequently underdiagnosed, particularly in patients presenting with depressive disorders, leading to delays in appropriate treatment. Artificial intelligence (AI) applied to electronic health records (EHRs) may improve early detection by identifying clinically relevant symptom patterns. ObjectiveTo evaluate the diagnostic performance of a natural language processing (NLP)-based AI model for detecting BD-related features in EHRs of patients with affective diagnoses. MethodsA retrospective diagnostic accuracy study was conducted using 500 EHRs from a psychiatric referral hospital in Bogota, Colombia (2020-2024). The model extracted 18 predefined clinical doma