AI detects bipolar disorder in EHRs of patients with affective diagnoses with AUC 0.93
medRxiv diagnostic accuracy preprint: EHR model reports AUC 0.93, sensitivity 96.4%, and specificity 84.4%.
AI detects bipolar disorder in EHRs of patients with affective diagnoses with AUC 0.93
Original title: Artificial intelligence for detecting bipolar disorder in electronic health records of patients with affective diagnoses: a diagnostic accuracy study
Authors: Eugenio Ferro, Natalia Castaño-Villegas, Manuel F. Esteban Cárdenas, María Gómez Puentes, Carlos Torres-Delgado, Laura Ortiz Calderon, Katherine Monsalve, José Zea
Venue: medRxiv preprint — manuscript ID 2026.05.07.26352679v1
Status: Preprint, under journal review
Headline metrics: n=500 EHRs; validation subset n=100; AUC 0.93; sensitivity 96.4% (95% CI: 87.7–99.0); specificity 84.4% (95% CI: 71.2–92.3); domain-level concordance 91.3% (95% CI: 89.9–92.6); 36.4% of 387 patients without prior bipolar diagnosis newly classified as at risk
This diagnostic accuracy study evaluated artificial intelligence for detecting bipolar disorder in electronic health records from patients with affective diagnoses. The reported dataset included n=500 EHRs and a validation subset of n=100, following STARD 2015 and TRIPOD-AI reporting standards.
Headline metrics include AUC 0.93, sensitivity 96.4% (95% CI: 87.7–99.0), specificity 84.4% (95% CI: 71.2–92.3), and domain-level concordance 91.3% (95% CI: 89.9–92.6). Among 387 patients without a prior bipolar diagnosis, 36.4% were newly classified as at risk.
Source
https://www.medrxiv.org/content/10.64898/2026.05.07.26352679v1