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Screening for Psychological Distress in Healthcare Workers Using Machine Learning: A Proof of Concept.

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Machine learning models can identify healthcare workers at risk for anxiety, depression, and PTSD using just two questions and biological sex. Further data is needed to improve model accuracy for mental health screening.

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Area of Science:

  • Psychiatry and Mental Health
  • Computational Medicine
  • Epidemiology

Background:

  • Healthcare workers faced significant psychological distress during the COVID-19 pandemic.
  • Early identification of mental health conditions like anxiety, depression, and PTSD is crucial for timely intervention.
  • Existing screening methods can be burdensome for frequent monitoring.

Purpose of the Study:

  • To develop and validate preliminary machine learning models for identifying healthcare workers at risk of anxiety, depression, and PTSD.
  • To assess the feasibility of using minimal data (two questions and biological sex) for psychological distress screening.
  • To explore the potential of reducing assessment burden through data-driven models.

Main Methods:

  • Prospective cohort study of 816 healthcare workers during the first two waves of COVID-19.
  • Weekly data collection via mobile application, including 11 questions and three validated screening questionnaires.
  • Development and validation of logistic regression and support vector machine models using fivefold cross-validation.
  • Feature selection focused on two key questions and biological sex.

Main Results:

  • Machine learning models achieved 70% to 80% accuracy in identifying positive screening scores for anxiety, depression, and PTSD.
  • The positive predictive value of the models did not exceed 50%, indicating a need for more data.
  • The study demonstrated proof of concept for using machine learning with limited data for screening.

Conclusions:

  • Machine learning offers a feasible approach to developing novel screening models for psychological distress in healthcare workers.
  • Models utilizing minimal data show promise in reducing the burden of weekly assessments.
  • Further research with larger datasets is necessary to enhance the predictive accuracy and clinical utility of these models.