@article{ref1,
title="Predicting internalizing symptoms with machine learning: identifying individuals that need care",
journal="Journal of American college health",
year="2023",
author="Wang, Mengxing and Richmond, Lauren L. and Schleider, Jessica L. and Nelson, Brady D. and Luhmann, Christian C.",
volume="ePub",
number="ePub",
pages="ePub-ePub",
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. <br><br>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. <br><br>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. <br><br>CONCLUSIONS: Overall, the study suggests that predictive models can be clinically useful in identifying individuals with internalizing symptoms based on daily life disruption experiences.<p /> <p>Language: en</p>",
language="en",
issn="0744-8481",
doi="10.1080/07448481.2023.2277185",
url="http://dx.doi.org/10.1080/07448481.2023.2277185"
}