Biomarker discovery using machine learning in the psychosis spectrum

Abstract

The past decade witnessed substantial discoveries related to the psychosis spectrum. Many of these discoveries resulted from pursuits of objective and quantifiable biomarkers in tandem with the application of analytical tools such as machine learning. These approaches provided exciting new insights that significantly helped improve precision in diagnosis, prognosis, and treatment. This article provides an overview of how machine learning has been employed in recent biomarker discovery research in the psychosis spectrum, which includes schizophrenia, schizoaffective disorders, bipolar disorder with psychosis, first episode psychosis, and clinical high risk for psychosis. It highlights both human and animal model studies and explores a varying range of the most impactful biomarkers including cognition, neuroimaging, electrophysiology, and digital markers. We specifically highlight new applications and opportunities for machine learning to impact noninvasive symptom monitoring, prediction of future diagnosis and treatment outcomes, integration of new methods with traditional clinical research and practice, and personalized medicine approaches.

Keywords: Bipolar disorder; Clinical high risk; First episode; Machine learning; Psychosis; Schizophrenia.