Scientists identify workflow algorithm to predict psychosis

Date:
January 11, 2021
Source:
Max-Planck-Gesellschaft
Summary:
Cleverly combining artificial and human intelligence leads to
improved prevention of psychosis in young patients.



FULL STORY ========================================================================== Scientists from the Max Planck Institute of Psychiatry, led by Nikolaos Koutsouleris, combined psychiatric assessments with machine-learning
models that analyse clinical and biological data. Although psychiatrists
make very accurate predictions about positive disease outcomes, they might underestimate the frequency of adverse cases that lead to relapses. The algorithmic pattern recognition helps physicians to better predict the
course of disease.


==========================================================================
The results of the study show that it is the combination of artificial and human intelligence that optimizes the prediction of mental illness. "This algorithm enables us to improve the prevention of psychosis, especially in young patients at high risk or with emerging depression, and to intervene
in a more targeted and well-timed manner" explains Koutsouleris.

The algorithm does not replace treatment by medical professionals;
rather, it assists decision making and provides recommendations as to
whether to conduct further examinations on an individual basis. Using
the algorithm, practitioners can identify at an early stage the patients
that need therapeutic intervention and those who do not. "The results
of our study could help drive a reciprocal and interactive process of
clinical validation and improve prognostic tools in real-world screening services," Koutsouleris summarizes.


========================================================================== Story Source: Materials provided by Max-Planck-Gesellschaft. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Nikolaos Koutsouleris, Dominic B. Dwyer, Franziska Degenhardt,
Carlo Maj,
Maria Fernanda Urquijo-Castro, Rachele Sanfelici, David Popovic,
Oemer 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 K.

Haidl, Marlene Rosen, Katharine Chisholm, Anita Riecher-Ro"ssler,
Laura Egloff, Andre' Schmidt, Christina Andreou, Jarmo Hietala,
Timo Schirmer, Georg Romer, Petra Walger, Maurizia Franscini,
Nina Traber-Walker, Benno G. Schimmelmann, Rahel Flu"ckiger,
Chantal Michel, Wulf Ro"ssler, Oleg Borisov, Peter M. Krawitz,
Karsten Heekeren, Roman Buechler, Christos Pantelis, Peter Falkai,
Raimo K. R. Salokangas, Rebekka Lencer, Alessandro Bertolino,
Stefan Borgwardt, Markus Noethen, Paolo Brambilla, Stephen J. Wood,
Rachel Upthegrove, Frauke Schultze-Lutter, Anastasia Theodoridou,
Eva Meisenzahl. Multimodal Machine Learning Workflows for Prediction
of Psychosis in Patients With Clinical High-Risk Syndromes and
Recent-Onset Depression. JAMA Psychiatry, 2020; DOI: 10.1001/
jamapsychiatry.2020.3604 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/01/210111094301.htm

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