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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, https://ror.org/040r8fr65Aarhus University Hospital – Psychiatry, Aarhus, Denmark Department of Clinical Medicine, https://ror.org/040r8fr65Aarhus University, Aarhus, Denmark
Jakob Grøhn Damgaard
Affiliation:
Department of Affective Disorders, https://ror.org/040r8fr65Aarhus University Hospital – Psychiatry, Aarhus, Denmark Department of Clinical Medicine, https://ror.org/040r8fr65Aarhus University, Aarhus, Denmark
Martin Bernstorff
Affiliation:
Department of Affective Disorders, https://ror.org/040r8fr65Aarhus University Hospital – Psychiatry, Aarhus, Denmark Department of Clinical Medicine, https://ror.org/040r8fr65Aarhus University, Aarhus, Denmark
Lasse Hansen
Affiliation:
Department of Affective Disorders, https://ror.org/040r8fr65Aarhus University Hospital – Psychiatry, Aarhus, Denmark Department of Clinical Medicine, https://ror.org/040r8fr65Aarhus University, Aarhus, Denmark
Andreas Aalkjær Danielsen
Affiliation:
Department of Affective Disorders, https://ror.org/040r8fr65Aarhus University Hospital – Psychiatry, Aarhus, Denmark Department of Clinical Medicine, https://ror.org/040r8fr65Aarhus University, Aarhus, Denmark
Søren Dinesen Østergaard
Affiliation:
Department of Affective Disorders, https://ror.org/040r8fr65Aarhus University Hospital – Psychiatry, Aarhus, Denmark Department of Clinical Medicine, https://ror.org/040r8fr65Aarhus 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

When this article was originally published in Psychological Medicine it contained a couple of errors in the methods section of the manuscript and Supplementary Table 1.

In the following paragraph in the methods section under the “Predictor engineering and lookbehind window” subheading:

This model is bound by a maximum input sequence length of 512 tokens. For each patient, the first 512 tokens from each clinical note within the 180 days lookbehind prior to a prediction time were extracted and input to the model, yielding a contextualized embedding of the text with 384 dimensions.”

“512 tokens” has been replaced with “128 tokens”, such that the paragraph will read as follows:

This model is bound by a maximum input sequence length of 128 tokens. For each patient, the first 128 tokens from each clinical note within the 180 days lookbehind prior to a prediction time were extracted and input to the model, yielding a contextualized embedding of the text with 384 dimensions.”

In Supplementary Table 1, under “Text features/embeddings”, the following text:

“Embeddings, each with 384 dimensions, were generated from the first 512 tokens of each note within the 180 days window These were then averaged to create an aggregate embedding with 384 dimensions.”

Has been replaced with:

“Embeddings, each with 384 dimensions, were generated from the first 128 tokens of each note within the 180 days window. These were then averaged to create an aggregate embedding with 384 dimensions.”

The authors apologise for this error.

References

Perfalk, E., Damgaard, J. G., Bernstorff, M., Hansen, L., Danielsen, A. A., & Østergaard, S. D. (2025). Predicting involuntary admission following inpatient psychiatric treatment using machine learning trained on electronic health record data. Psychological Medicine. 54(15):43484361. https://doi.org/10.1017/S0033291724002642Google Scholar