Identifying Adolescent Depression and Anxiety Through Real-World Data and Social Determinants of Health: Machine Learning Model Development and Validation.

ABSTRACT:

Background

The prevalence of adolescent mental health conditions such as depression and anxiety has significantly increased. Despite the potential of machine learning (ML), there is a shortage of models that use real-world data (RWD) to enhance early detection and intervention for these conditions.

Objective

This study aimed to identify depression and anxiety in adolescents using ML techniques on RWD and social determinants of health (SDoH).

Methods

We analyzed RWD of adolescents aged 10-17 years, considering various factors such as demographics, prior diagnoses, prescribed medications, medical procedures, and laboratory measurements recorded before the onset of anxiety or depression. Clinical data were linked with SDoH at the block-level. Three separate models were d

SUBMITTER: Mardini MT 

PROVIDER: S-EPMC11838812 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature