Early Risk Prediction in Mental Health: From Data to Clinic

Soochow University, Suzhou Medical College, 215123 Dushu Lake Campus, Suzhou, Jiangsu, China

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

Mental disorders occur early and are highly disabling. Traditional diagnosis relies on the full manifestation of symptoms, often missing the window for early intervention. This review integrates literature from computational psychiatry, machine learning, natural language processing, and clinical epidemiology to systematically review four dimensions of early risk prediction. Research has found that multimodal data fusion and natural language processing techniques can significantly improve predictive performance; Social determinants not only improve model accuracy, but also help narrow the prediction gap between different populations. However, existing models generally suffer from issues such as insufficient external validity, poor interpretability, lagging ethical supervision, and weak intervention connections. In the future, collaborative efforts are needed in areas such as federated learning, grey box modeling, fairness assessment, and cross departmental collaboration to truly move early risk prediction from technically feasible to clinically meaningful, achieving the ultimate goal of providing effective assistance to people at risk.