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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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References

Brooks, SK, Webster, RK, Smith, LE, Woodland, L, Wessely, S, Greenberg, N and Rubin, GJ (2020) The psychological impact of quarantine and how to reduce it: Rapid review of the evidence. The Lancet 395(10227), 912–920.CrossRefGoogle Scholar
Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., Grobler, J., Layton, R., VanderPlas, J., Joly, A., Holt, B. and Varoquaux, G. (2013) API design for machine learning software: experiences from the scikit-learn project. In: ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pp. 108–122.Google Scholar
Chen, T and Guestrin, C (2016) Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794.CrossRefGoogle Scholar
Hansen, L, Enevoldsen, KC, Bernstorff, M, Nielbo, KL, Danielsen, AA and Østergaard, SD (2021) The PSYchiatric clinical outcome prediction (PSYCOP) cohort: leveraging the potential of electronic health records in the treatment of mental disorders. Acta Neuropsychiatrica 33(6), 1–27.CrossRefGoogle ScholarPubMed
He, L, Wei, D, Yang, F, Zhang, J, Cheng, W, Feng, J, Yang, W, Zhuang, K, Chen, Q, Ren, Z, Li, Y, Wang, X, Mao, Y, Chen, Z, Liao, M, Cui, H, Li, C, He, Q, Lei, X, Qiu, J and et al. (2021) Functional connectome prediction of anxiety related to the COVID-19 pandemic. The American Journal of Psychiatry 178, 530–540. doi: 10.1176/appi.ajp.2020.20070979.CrossRefGoogle ScholarPubMed
Jefsen, OH, Rohde, C, Nørremark, B and Østergaard, SD (2020) COVID-19-related self-harm and suicidality among individuals with mental disorders. Acta Psychiatrica Scandinavica 142(2), 152–153.CrossRefGoogle ScholarPubMed
Lemaître, G, Nogueira, F and Aridas, CK (2017) Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning. Journal of Machine Learning Research 18(17), 1–5.Google Scholar
Rohde, C, Jefsen, OH, Nørremark, B, Danielsen, AA and Østergaard, SD (2020) Psychiatric symptoms related to the COVID-19 pandemic. Acta Neuropsychiatrica 32(5), 274–276.CrossRefGoogle ScholarPubMed
Saunders, R, Buckman, JEJ, Leibowitz, J, Cape, J and Pilling, S (2021) Trends in depression & anxiety symptom severity among mental health service attendees during the COVID-19 pandemic. Journal of Affective Disorders 289, 105–109.CrossRefGoogle ScholarPubMed
SSI (2021) Covid19.SSI.dk, The Danish State Serum Institute’s (SSI) webpage containing Covid-19 data from Denmark. Available at https://covid19.ssi.dk/ Google Scholar
Sønderskov, KM, Dinesen, PT, Vistisen, HT and Østergaard, SD (2021) Variation in psychological well-being and symptoms of anxiety and depression during the COVID-19 pandemic: results from a three-wave panel survey. Acta Neuropsychiatrica 33, 156–159.CrossRefGoogle ScholarPubMed
Szcześniak, D, Gładka, A, Misiak, B, Cyran, A and Rymaszewska, J (2021) The SARS-CoV-2 and mental health: from biological mechanisms to social consequences. Progress in Neuro-Psychopharmacology and Biological Psychiatry 104, 110046.CrossRefGoogle ScholarPubMed
Zhang, H (2004) The optimality of naive Bayes. In: Proceedings of 17th International Florida Artificial Intelligence Research Society Conference, Menlo Park, pp. 562–567.Google Scholar
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