Detecting relapse in youth with psychotic disorders utilizing patient-generated and patient-contributed digital data from Facebook.

ABSTRACT: Although most patients who experience a first-episode of psychosis achieve remission of positive psychotic symptoms, relapse is common. Existing relapse evaluation strategies are limited by their reliance on direct and timely contact with professionals, and accurate reporting of symptoms. A method by which to objectively identify early relapse warning signs could facilitate swift intervention. We collected 52,815 Facebook posts across 51 participants with recent onset psychosis (mean age = 23.96 years; 70.58% male) and applied anomaly detection to explore linguistic and behavioral changes associated with psychotic relapse. We built a one-class classification model that makes patient-specific personalized predictions on risk to relapse. Significant differences were identified in the words p

SUBMITTER: Birnbaum ML 

PROVIDER: S-EPMC6779748 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature