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

N, Koutsouleris; DB, Dwyer; F, Degenhardt; C, Maj; MF, Urquijo-Castro; R, Sanfelici; D, Popovic; O, Oeztuerk; SS, Haas; J, Weiske; A, Ruef; L, Kambeitz-Ilankovic; LA, Antonucci; S, Neufang; C, Schmidt-Kraepelin; S, Ruhrmann; N, Penzel; J, Kambeitz; TK, Haidl; M, Rosen; K, Chisholm; A, Riecher-Rössler; L, Egloff; A, Schmidt; C, Andreou; J, Hietala; T, Schirmer; G, Romer; P, Walger; M, Franscini; N, Traber-Walker; BG, Schimmelmann; R, Flückiger; C, Michel; W, Rössler; O, Borisov; PM, Krawitz; K, Heekeren; R, Buechler; C, Pantelis; P, Falkai; RKR, Salokangas; R, Lencer; A, Bertolino; S, Borgwardt; M, Noethen; P, Brambilla; SJ, Wood; R, Upthegrove; F, Schultze-Lutter; A, Theodoridou; E, Meisenzahl

doi:10.1001/jamapsychiatry.2020.3604

Public

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

Shared by NobleBlocks on Feb 1, 2021 • 12:00 AM UTC

Authors:

Koutsouleris N

Dwyer DB

Degenhardt F

Abstract

<h4>Importance</h4>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 rema...

Research Assistant

AI chat, annotations, notes & similar papers

Finding related papers...

Discussions

(0)

No comments yet

Be the first to share your thoughts!