Individualized prediction of psychosis in subjects with an at-risk mental state

Early intervention strategies in psychosis would significantly benefit from the identification of reliable prognostic biomarkers. Pattern classification methods have shown the feasibility of an early diagnosis of psychosis onset both in clinical and familial high-risk populations. Here we were interested in replicating our previous classification findings using an independent cohort at clinical high risk for psychosis, drawn from the prospective FePsy (Fruherkennung von Psychosen) study. The same neuroanatomical-based pattern classification pipeline, consisting of a linear Support Vector Machine (SVM) and a Recursive Feature Selection (RFE) achieved 74% accuracy in predicting later onset of psychosis.

Source: Schizophrenia Research - September 19, 2017 Category: Psychiatry Authors: Eleni Zarogianni, Amos J. Storkey, Stefan Borgwardt, Renata Smieskova, Erich Studerus, Anita Riecher-R össler, Stephen M. Lawrie Source Type: research