Individualized prediction of three- and six-year outcomes of psychosis in a longitudinal multicenter study: a machine learning approach.

ABSTRACT: Schizophrenia and related disorders have heterogeneous outcomes. Individualized prediction of long-term outcomes may be helpful in improving treatment decisions. Utilizing extensive baseline data of 523 patients with a psychotic disorder and variable illness duration, we predicted symptomatic and global outcomes at 3-year and 6-year follow-ups. We classified outcomes as (1) symptomatic: in remission or not in remission, and (2) global outcome, using the Global Assessment of Functioning (GAF) scale, divided into good (GAF ≥ 65) and poor (GAF < 65). Aiming for a robust and interpretable prediction model, we employed a linear support vector machine and recursive feature elimination within a nested cross-validation design to obtain a lean set of predictors. Generalization to out-of-study sampl

SUBMITTER: de Nijs J 

PROVIDER: S-EPMC8253813 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature