Monitoring of COVID-19 Pandemic-related Psychopathology using Machine Learning

Kenneth Enevoldsen; Andreas Aalkjær Danielsen; Christopher Rohde; Oskar Hougaard Jefsen; Kristoffer Laigaard Nielbo; Søren Dinesen Østergaard

Monitoring of COVID-19 Pandemic-related Psychopathology using Machine Learning

Kenneth Enevoldsen^*, Andreas Aalkjær Danielsen, Christopher Rohde, Oskar Hougaard Jefsen, Kristoffer Laigaard Nielbo, Søren Dinesen Østergaard

^*Corresponding author for this work

Research output: Working paper/Preprint › Preprint

Abstract

The COVID-19 pandemic has been shown to have a major negative impact on global mental health and patients with mental illness may be particularly vulnerable. We show that developments in COVID-19 pandemic-related psychopathology among patients with mental illness can be meaningfully monitored using machine learning methods. The COVID-19 pandemic-related psychopathology was found to covary with the pandemic pressure. This correlation was, however, less pronounced during the second wave compared to the first wave of the pandemic - possibly due to habituation.

Original language	English
Publisher	medRxiv
Publication status	Published - 2021

Keywords

Coronavirus
Natural Language Processing
Machine Learning
Mental Disorders
Pandemic
COVID-19

Access to Document

https://www.medrxiv.org/content/10.1101/2021.07.13.21259962v1

1 Journal article
1 Letter

Psychological well-being and symptoms of depression and anxiety across age groups during the second wave of the COVID-19 Pandemic in Denmark
Vistisen, H. T., Sønderskov, K. M., Dinesen, P. T. & Østergaard, S. D., Dec 2021, In: Acta Neuropsychiatrica. 33, 6, p. 331-334 4 p.
Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Letter › peer-review
The PSYchiatric Clinical Outcome Prediction (PSYCOP) cohort: Leveraging the potential of electronic health records in the treatment of mental disorders
Hansen, L., Enevoldsen, K. C., Bernstorff, M., Nielbo, K. L., Danielsen, A. A. & Østergaard, S. D., Dec 2021, In: Acta Neuropsychiatrica. 33, 6, p. 323 - 330 8 p.
Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

Cite this

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title = "Monitoring of COVID-19 Pandemic-related Psychopathology using Machine Learning",

abstract = "The COVID-19 pandemic has been shown to have a major negative impact on global mental health and patients with mental illness may be particularly vulnerable. We show that developments in COVID-19 pandemic-related psychopathology among patients with mental illness can be meaningfully monitored using machine learning methods. The COVID-19 pandemic-related psychopathology was found to covary with the pandemic pressure. This correlation was, however, less pronounced during the second wave compared to the first wave of the pandemic - possibly due to habituation.",

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author = "Kenneth Enevoldsen and Danielsen, {Andreas Aalkj{\ae}r} and Christopher Rohde and Jefsen, {Oskar Hougaard} and Nielbo, {Kristoffer Laigaard} and {\O}stergaard, {S{\o}ren Dinesen}",

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T1 - Monitoring of COVID-19 Pandemic-related Psychopathology using Machine Learning

AU - Enevoldsen, Kenneth

AU - Danielsen, Andreas Aalkjær

AU - Rohde, Christopher

AU - Jefsen, Oskar Hougaard

AU - Nielbo, Kristoffer Laigaard

AU - Østergaard, Søren Dinesen

PY - 2021

Y1 - 2021

N2 - The COVID-19 pandemic has been shown to have a major negative impact on global mental health and patients with mental illness may be particularly vulnerable. We show that developments in COVID-19 pandemic-related psychopathology among patients with mental illness can be meaningfully monitored using machine learning methods. The COVID-19 pandemic-related psychopathology was found to covary with the pandemic pressure. This correlation was, however, less pronounced during the second wave compared to the first wave of the pandemic - possibly due to habituation.

AB - The COVID-19 pandemic has been shown to have a major negative impact on global mental health and patients with mental illness may be particularly vulnerable. We show that developments in COVID-19 pandemic-related psychopathology among patients with mental illness can be meaningfully monitored using machine learning methods. The COVID-19 pandemic-related psychopathology was found to covary with the pandemic pressure. This correlation was, however, less pronounced during the second wave compared to the first wave of the pandemic - possibly due to habituation.

KW - Coronavirus

KW - Natural Language Processing

KW - Machine Learning

KW - Mental Disorders

KW - Pandemic

KW - COVID-19

M3 - Preprint

BT - Monitoring of COVID-19 Pandemic-related Psychopathology using Machine Learning

PB - medRxiv

ER -

Monitoring of COVID-19 Pandemic-related Psychopathology using Machine Learning

Abstract

Keywords

Access to Document

Fingerprint

Research output

Psychological well-being and symptoms of depression and anxiety across age groups during the second wave of the COVID-19 Pandemic in Denmark

The PSYchiatric Clinical Outcome Prediction (PSYCOP) cohort: Leveraging the potential of electronic health records in the treatment of mental disorders

Cite this