Screening for Psychological Distress in Healthcare Workers Using Machine Learning: A Proof of Concept.
The purpose of this study was to train and test preliminary models using two machine learning algorithms to identify healthcare workers at risk of developing anxiety, depression, and post-traumatic stress disorder. The study included data from a prospective cohort study of 816 healthcare workers col...
| Publicado en: | Journal of Medical Systems Vol. 47; no. 1; pp. 1 - 10 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
11/16/2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=173963178&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173963178 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 11/16/2023 vid: 47 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 173963178 173963178 173963178 10.1007/s10916-023-02011-5 173963178 ppf: 1 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Screening for Psychological Distress in Healthcare Workers Using Machine Learning: A Proof of Concept. aug: au: Geoffrion, Steve Morse, Catherine Dufour, Marie-Michèle Bergeron, Nicolas Guay, Stéphane Lanovaz, Marc J. affil: Research center of l'Institut universitaire en santé mentale de Montréal (CR-IUSMM), Montréal, Québec, Canada sug: subj: Psychological Distress Evaluation Health Screening Health Personnel Psychosocial Factors Machine Learning Anxiety Diagnosis Depression Diagnosis Stress Disorders, Post-Traumatic Diagnosis Human Algorithms Quebec Canada Descriptive Statistics Support Vector Machine Linear Regression Funding Source Theory Construction Scales Questionnaires Checklists Logistic Regression Male Female Male Female ab: The purpose of this study was to train and test preliminary models using two machine learning algorithms to identify healthcare workers at risk of developing anxiety, depression, and post-traumatic stress disorder. The study included data from a prospective cohort study of 816 healthcare workers collected using a mobile application during the first two waves of COVID-19. Each week, the participants responded to 11 questions and completed three screening questionnaires (one for anxiety, one for depression, and one for post-traumatic stress disorder). Then, the research team selected two questions (out of the 11), which were used with biological sex to identify whether scores on each screening questionnaire would be positive or negative. The analyses involved a fivefold cross-validation to test the accuracy of models based on logistic regression and support vector machines using cross-sectional and cumulative measures. The findings indicated that the models derived from the two questions and biological sex accurately identified screening scores for anxiety, depression, and post-traumatic stress disorders in 70% to 80% of cases. However, the positive predictive value never exceeded 50%, underlining the importance of collecting more data to train better models. Our proof of concept demonstrates the feasibility of using machine learning to develop novel models to screen for psychological distress in at-risk healthcare workers. Developing models with fewer questions may reduce burdens of active monitoring in practical settings by decreasing the weekly assessment duration. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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