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

Nikolaos Koutsouleris | Zendy; Dominic Dwyer | Zendy; Franziska Degenhardt | Zendy; Carlo Maj | Zendy; Maria Fernanda Urquijo-Castro | Zendy; Rachele Sanfelici | Zendy; David Popovic | Zendy; Oemer Faruk Oeztuerk | Zendy; Shalaila S. Haas | Zendy; Johanna Weiske | Zendy; Anne Ruef | Zendy; Lana KambeitzIlankovic | Zendy; Linda A. Antonucci | Zendy; Susanne Neufang | Zendy; Christian Schmidt-Kraepelin | Zendy; Stephan Ruhrmann | Zendy; Nora Penzel | Zendy; Joseph Kambeitz | Zendy; Theresa Haidl | Zendy; Marlene Rosen | Zendy; Katharine Chisholm | Zendy; Anita RiecherRössler | Zendy; Laura Egloff | Zendy; André Schmidt | Zendy; Christina Andreou | Zendy; Jarmo Hietala | Zendy; Timo Schirmer | Zendy; Georg Romer | Zendy; Petra Walger | Zendy; Maurizia Franscini | Zendy; Nina TraberWalker | Zendy; Benno G. Schimmelmann | Zendy; Rahel Flückiger | Zendy; Chantal Michel | Zendy; Wulf Rössler | Zendy; Oleg Borisov | Zendy; Peter Krawitz | Zendy; Karsten Heekeren | Zendy; Roman Buechler | Zendy; Christos Pantelis | Zendy; Peter Falkai | Zendy; Raimo K. R. Salokangas | Zendy; Rebekka Lencer | Zendy; Alessandro Bertolino | Zendy; Stefan Borgwardt | Zendy; Markus M. Nöethen | Zendy; Paolo Brambilla | Zendy; Stephen J. Wood | Zendy; Rachel Upthegrove | Zendy; Frauke SchultzeLutter | Zendy; Anastasia Theodoridou | Zendy; Eva Meisenzahl | Zendy

Open Access

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

Author(s) -

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 KambeitzIlankovic,

Linda A. Antonucci,

Susanne Neufang,

Christian Schmidt-Kraepelin,

Stephan Ruhrmann,

Nora Penzel,

Joseph Kambeitz,

Theresa Haidl,

Marlene Rosen,

Katharine Chisholm,

Anita RiecherRössler,

Laura Egloff,

André Schmidt,

Christina Andreou,

Jarmo Hietala,

Timo Schirmer,

Georg Romer,

Petra Walger,

Maurizia Franscini,

Nina TraberWalker,

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 SchultzeLutter,

Anastasia Theodoridou,

Eva Meisenzahl

Publication year - 2020

Publication title -

jama psychiatry

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 7.531

H-Index - 365

eISSN - 2168-6238

pISSN - 2168-622X

DOI - 10.1001/jamapsychiatry.2020.3604

Subject(s) - neurocognitive , generalizability theory , psychosis , schizophrenia (object oriented programming) , depression (economics) , neuroimaging , sample size determination , prodrome , psychology , medicine , psychiatry , clinical psychology , cognition , developmental psychology , statistics , mathematics , economics , macroeconomics

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 unclear.

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