Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression

IMPORTANCE: Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to young patients with depressive syndromes remains unc...

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Detalles Bibliográficos
Autores principales: Koutsouleris, Nikolaos, Dwyer, Dominic B., Degenhardt, Franziska, Maj, Carlo, Urquijo-Castro, Maria Fernanda, Sanfelici, Rachele, Popovic, David, Oeztuerk, Oemer, Haas, Shalaila S., Weiske, Johanna, Ruef, Anne, Kambeitz-Ilankovic, Lana, Antonucci, Linda A., Neufang, Susanne, Schmidt-Kraepelin, Christian, Ruhrmann, Stephan, Penzel, Nora, Kambeitz, Joseph, Haidl, Theresa K., Rosen, Marlene, Chisholm, Katharine, Riecher-Rössler, Anita, Egloff, Laura, Schmidt, André, Andreou, Christina, Hietala, Jarmo, Schirmer, Timo, Romer, Georg, Walger, Petra, Franscini, Maurizia, Traber-Walker, Nina, Schimmelmann, Benno G., Flückiger, Rahel, Michel, Chantal, Rössler, Wulf, Borisov, Oleg, Krawitz, Peter M., Heekeren, Karsten, Buechler, Roman, Pantelis, Christos, Falkai, Peter, Salokangas, Raimo K. R., Lencer, Rebekka, Bertolino, Alessandro, Borgwardt, Stefan, Noethen, Markus, Brambilla, Paolo, Wood, Stephen J., Upthegrove, Rachel, Schultze-Lutter, Frauke, Theodoridou, Anastasia, Meisenzahl, Eva
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Association 2020
Materias:
Original Investigation
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7711566/
https://www.ncbi.nlm.nih.gov/pubmed/33263726
http://dx.doi.org/10.1001/jamapsychiatry.2020.3604

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