Journal article

Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or with Recent-Onset Depression: A Multimodal, Multisite Machine Learning Analysis

N Koutsouleris, L Kambeitz-Ilankovic, S Ruhrmann, M Rosen, A Ruef, DB Dwyer, M Paolini, K Chisholm, J Kambeitz, T Haidl, A Schmidt, J Gillam, F Schultze-Lutter, P Falkai, M Reiser, A Riecher-Rössler, R Upthegrove, J Hietala, RKR Salokangas, C Pantelis Show all

JAMA Psychiatry | AMER MEDICAL ASSOC | Published : 2018

Abstract

Importance: Social and occupational impairments contribute to the burden of psychosis and depression. There is a need for risk stratification tools to inform personalized functional-disability preventive strategies for individuals in at-risk and early phases of these illnesses. Objective: To determine whether predictors associated with social and role functioning can be identified in patients in clinical high-risk (CHR) states for psychosis or with recent-onset depression (ROD) using clinical, imaging-based, and combined machine learning; assess the geographic, transdiagnostic, and prognostic generalizability of machine learning and compare it with human prognostication; and explore sequenti..

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Grants

Awarded by National Institute of Mental Health


Funding Acknowledgements

PRONIA is a Collaborative Project funded by the European Union under the 7th Framework Programme (grant 602152). Dr Pantelis was supported by National Health and Medical Research Council Senior Principal Research Fellowship (grants 628386 and 1105825) and European Union-National Health and Medical Research Council (grant 1075379).