First-episode psychosis: predicting outcomes using machine learning approach

Background At present, no tools exist to estimate objectively the risk of poor treatment outcomes in patients with first episode psychosis. Such tools could improve treatment by informing clinical decision-making before the commencement of treatment. We tested whether such a tool could be successfully built and validated using routinely available, patient-reportable information. Methods By applying machine learning to data from 334 patients in the European First Episode Schizophrenia Trial (EUFEST; International Clinical Trials Registry Platform number, ISRCTN68736636), we developed a tool to predict poor versus good treatment outcome (Global Assessment of Functioning [GAF] score ≥65 vs GAF <65, respectively) after 4 weeks and 52 weeks of treatment. To enable the unbiased estimation of the predictive system’s generalisability to new patients, we used repeated nested cross-validation to prevent information leaking between…


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