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Predicting involuntary admission following inpatient psychiatric treatment using machine learning trained on electronic health record data – CORRIGENDUM

Published online by Cambridge University Press:  16 June 2025

Erik Perfalk*
Affiliation:
Department of Affective Disorders, Aarhus University Hospital – Psychiatry, Aarhus, Denmark Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
Jakob Grøhn Damgaard
Affiliation:
Department of Affective Disorders, Aarhus University Hospital – Psychiatry, Aarhus, Denmark Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
Martin Bernstorff
Affiliation:
Department of Affective Disorders, Aarhus University Hospital – Psychiatry, Aarhus, Denmark Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
Lasse Hansen
Affiliation:
Department of Affective Disorders, Aarhus University Hospital – Psychiatry, Aarhus, Denmark Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
Andreas Aalkjær Danielsen
Affiliation:
Department of Affective Disorders, Aarhus University Hospital – Psychiatry, Aarhus, Denmark Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
Søren Dinesen Østergaard
Affiliation:
Department of Affective Disorders, Aarhus University Hospital – Psychiatry, Aarhus, Denmark Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
*
Corresponding author: Erik Perfalk; Email: erperf@rm.dk
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Abstract

Information

Type
Corrigendum
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0), which permits non-commercial re-use, distribution, and reproduction in any medium, provided that no alterations are made and the original article is properly cited. The written permission of Cambridge University Press must be obtained prior to any commercial use and/or adaptation of the article.
Copyright
© The Author(s), 2025. Published by Cambridge University Press