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Monitoring of COVID-19 pandemic-related psychopathology using machine learning

Published online by Cambridge University Press:  19 January 2022

Kenneth C. Enevoldsen Open the ORCID record for Kenneth C. Enevoldsen [Opens in a new window] ,
Andreas A. Danielsen Open the ORCID record for Andreas A. Danielsen [Opens in a new window] ,
Christopher Rohde ,
Oskar H. Jefsen Open the ORCID record for Oskar H. Jefsen [Opens in a new window] ,
Kristoffer L. Nielbo Open the ORCID record for Kristoffer L. Nielbo [Opens in a new window]  and
Søren D. Østergaard Open the ORCID record for Søren D. Østergaard [Opens in a new window]
Kenneth C. Enevoldsen*
Affiliation:
Center for Humanities Computing Aarhus, Aarhus University, Aarhus, Denmark Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
Andreas A. Danielsen
Affiliation:
Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
Christopher Rohde
Affiliation:
Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
Oskar H. Jefsen
Affiliation:
Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
Kristoffer L. Nielbo
Affiliation:
Center for Humanities Computing Aarhus, Aarhus University, Aarhus, Denmark Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
Søren D. Østergaard
Affiliation:
Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
*
Author for correspondence: Kenneth C. Enevoldsen, Email: kenneth.enevoldsen@cas.au.dk
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Abstract

The COVID-19 pandemic is believed to have a major negative impact on global mental health due to the viral disease itself as well as the associated lockdowns, social distancing, isolation, fear, and increased uncertainty. Individuals with preexisting mental illness are likely to be particularly vulnerable to these conditions and may develop outright ‘COVID-19-related psychopathology’. Here, we trained a machine learning model on structured and natural text data from electronic health records to identify COVID-19 pandemic-related psychopathology among patients receiving care in the Psychiatric Services of the Central Denmark Region. Subsequently, applying this model, we found that pandemic-related psychopathology covaries with the pandemic pressure over time. These findings may aid psychiatric services in their planning during the ongoing and future pandemics. Furthermore, the results are a testament to the potential of applying machine learning to data from electronic health records.

Keywords

coronavirusCOVID-19mental disordersmachine learningnatural language processing
Type
Short Communication
Information
Acta Neuropsychiatrica , Volume 34 , Issue 3 , June 2022 , pp. 148 - 152
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Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of Scandinavian College of Neuropsychopharmacology

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