Citation

Wang M, Richmond LL, Schleider JL, Nelson BD, Luhmann CC. J. Am. Coll. Health 2023; ePub(ePub): ePub.

Copyright

(Copyright © 2023, Informa - Taylor and Francis Group)

DOI

10.1080/07448481.2023.2277185

PMID

37943500

Abstract

OBJECTIVE The current project aims to identify individuals in urgent need of mental health care, using a machine learning algorithm (random forest). Comparison/contrast with conventional regression analyses is discussed. Participants: A total of 2,409 participants were recruited from an anonymous university, including undergraduate and graduate students, faculty, and staff.

METHODS: Answers to a COVID-19 impact survey, the Patient Health Questionnaire-9 (PHQ-9), and the Generalized Anxiety Disorder-7 (GAD-7) were collected. The total scores of PHQ-9 and GAD-7 were regressed on six composites that were created from the questionnaire items, based on their topics. A random forest was trained and validated.

RESULTS: Results indicate that the random forest model was able to make accurate, prospective predictions (R(2) =.429 on average) and we review variables that were deemed predictively relevant.

CONCLUSIONS: Overall, the study suggests that predictive models can be clinically useful in identifying individuals with internalizing symptoms based on daily life disruption experiences.

Language: en

Keywords

Anxiety; internalizing symptoms; depression; machine learning; random forest