Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression
JAMA Psychiatry2020Vol. 78(2), pp. 195–195
Citations Over TimeTop 1% of 2020 papers
Nikolaos Koutsouleris, Dominic Dwyer, Franziska Degenhardt, Carlo Maj, Maria Fernanda Urquijo-Castro, Rachele Sanfelici, David Popovic, Oemer Faruk Oeztuerk, Shalaila S. Haas, Johanna Weiske, Anne Ruef, Lana Kambeitz‐Ilankovic, Linda A. Antonucci, Susanne Neufang, Christian Schmidt-Kraepelin, Stephan Ruhrmann, Nora Penzel, Joseph Kambeitz, Theresa Haidl, Marlene Rosen, Katharine Chisholm, Anita Riecher‐Rössler, Laura Egloff, André Schmidt, Christina Andreou, Jarmo Hietala, Timo Schirmer, Georg Romer, Petra Walger, Maurizia Franscini, Nina Traber‐Walker, Benno G. Schimmelmann, Rahel Flückiger, Chantal Michel, Wulf Rössler, Oleg Borisov, Peter Krawitz, Karsten Heekeren, Roman Buechler, Christos Pantelis, Peter Falkai, Raimo K. R. Salokangas, Rebekka Lencer, Alessandro Bertolino, Stefan Borgwardt, Markus M. Nöethen, Paolo Brambilla, Stephen J. Wood, Rachel Upthegrove, Frauke Schultze‐Lutter, Anastasia Theodoridou, Eva Meisenzahl
Abstract
These findings suggest that psychosis transition can be predicted in a broader risk spectrum by sequentially integrating algorithms' and clinicians' risk estimates. For clinical translation, the proposed workflow should undergo large-scale international validation.
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