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Predicting internalizing symptoms with machine learning: identifying individuals that need care.

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Summary
This summary is machine-generated.

Machine learning accurately predicts mental health needs using daily disruption data. This approach aids in identifying students, faculty, and staff requiring urgent psychological support.

Keywords:
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Area of Science:

  • Psychology
  • Computer Science
  • Public Health

Background:

  • Mental health challenges are prevalent in university settings.
  • Identifying individuals in need of timely mental health care is crucial.
  • Existing methods may not fully capture the nuances of daily life disruptions impacting mental well-being.

Purpose of the Study:

  • To develop and validate a machine learning model for identifying individuals needing urgent mental health care.
  • To compare the efficacy of a random forest algorithm against traditional regression analyses.
  • To pinpoint specific daily life disruptions that predict internalizing symptoms.

Main Methods:

  • Recruited 2,409 university students, faculty, and staff.
  • Collected data using a COVID-19 impact survey, Patient Health Questionnaire-9 (PHQ-9), and Generalized Anxiety Disorder-7 (GAD-7).
  • Trained and validated a random forest model to predict PHQ-9 and GAD-7 scores based on survey composites.

Main Results:

  • The random forest model achieved accurate prospective predictions with an average R-squared of .429.
  • Identified key variables related to daily life disruptions that are predictive of mental health symptoms.
  • Demonstrated the model's ability to generalize and make reliable predictions.

Conclusions:

  • Predictive models, particularly random forests, show clinical utility in mental health care.
  • Daily life disruptions are significant indicators for identifying individuals with internalizing symptoms.
  • This approach offers a novel method for proactive mental health support in academic communities.