Prognostic predictions in psychosis: exploring the complementary role of machine learning models

Prognostic predictions in psychosis: exploring the complementary role of machine learning models

Background Predicting outcomes in schizophrenia spectrum disorders is challenging due to the variability of individual trajectories. While machine learning (ML) shows promise in outcome prediction, it has not yet been integrated into clinical practice. Understanding how ML models (MLMs) can compleme...

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Main Authors: Metten Somers, Hugo G Schnack, Rene S Kahn, Diane F van Rappard, Frank L Gerritse, Edwin van Dellen, Violet van Dee, Seyed M Kia, Caterina Fregosi, Wilma E Swildens, Anne Alkema, Albert Batalla, Coen van den Berg, Danko Coric, Lotte G Dijkstra, Arthur van den Doel, Livia S Dominicus, John Enterman, Marte Z van der Horst, Fedor van Houwelingen, Charlotte S Koch, Lisanne E M Koomen, Marjan Kromkamp, Michelle Lancee, Brian E Mouthaan, Eline J Regeer, Raymond W J Salet, Jorgen Straalman, Marjolein H T de Vette, Judith Voogt, Inge Winter-van Rossum, Wiepke Cahn
Format: Article
Language:English
Published: BMJ Publishing Group 2025-06-01
Series:BMJ Mental Health
Online Access:https://mentalhealth.bmj.com/content/28/1/e301594.full
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