Utilizing natural language processing for precision prevention of mental health disorders among youth: A systematic review

Abstract

Background

The global mental health crisis has created barriers to youth mental healthcare, leaving many disorders unaddressed. Precision prevention, which identifies individual risks, offers the potential for tailored interventions. While natural language processing (NLP) has shown promise in the early detection of mental health disorders, no review has examined its role in youth mental health detection. We hypothesize that NLP can improve early detection and personalized care in mental healthcare among youth.

Methodology

After screening 1197 articles from 5 databases, 12 papers were included covering six categories: (1) mental health disorders, (2) data sources, (3) NLP objective for mental health detection, (4) annotation and validation techniques, (5) linguistic markers, and (6) performance and evaluation. Study quality was assessed using Hawker's checklist for disparate study designs.

Results

Most studies focused on suicide risk (42 %), depression (25 %), and stress (17 %). Social media (42 %) and interviews (33 %) were the most common data sources, with linguistic inquiry and word count and support vector machines frequently used for analysis. While most studies were exploratory, one implemented a real-time tool for detecting mental health risks. Validation methods, including precision and recall metrics, showed strong predictive performance.

Conclusions

This review highlights the potential of NLP in youth mental health detection, addressing challenges such as bias, data quality, and ethical concerns. Future research should refine NLP models using diverse, multimodal datasets, addressing data imbalance, and improving real-time detection. Exploring transformer-based models and ensuring ethical, inclusive data handling will be key to advancing NLP-driven interventions.