Predicting multiple mental health outcomes in adolescents using explainable machine learning models
IntroductionAdolescent mental health problems, including depression, anxiety, and stress, are an increasing public health concern, yet the factors associated with different mental health outcomes may vary across domains. This study investigated shared and outcome-specific predictive features of depression, anxiety, perceived stress, and psychological well-being using explainable machine learning…
Read original article on Frontiers in Psychiatry | New and Recent Articles →Related news
-
10 Essential Ways to Defend Yourself Against Negativity
United States
-
110 Funny Letter Board Quotes That Will Make You Laugh Out Loud
Sweden
-
Waking Up to Life
United States
-
The Power of Beginner’s Mind
United States
-
Maternal emotional distress, caregiving burden, and service access among mothers of children with autism spectrum disorder: a qualitative study
Switzerland
-
Trauma-informed psychological support alongside long-acting injectable buprenorphine: clients’ perspectives on the Buvidal psychological support service (BPSS) pilot in Wales
Switzerland
-
Factors associated with school absenteeism in adolescents with mental illness: a multivariate analysis
Switzerland
-
Editorial: Intersectional perspectives on the lived experiences of people with intellectual disabilities across the lifespan
Switzerland