Scientists identify workflow algorithm to predict psychosis

Cleverly combining artificial and human intelligence leads to improved prevention of psychosis in young patients

Scientists from the Max Planck Institute of Psychiatry, led by Nikolaos Koutsouleris, combined psychiatric assessments with machine-learning models that analyse clinical and biological data. The results of the study, published in JAMA Psychiatry, show that it is the combination of artificial and human intelligence that optimizes the prediction of mental illness. The algorithm improves the prevention of psychosis, especially in young patients at high risk or with emerging depression and enables therapists to intervene in a more targeted and well-timed manner.

Nikolaos Koutsouleris; Dominic B. Dwyer; Franziska Degenhardt et al. (2021). Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression. JAMA Psychiatry. Online now.
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• Max Planck Institute of Psychiatry