Early warning model of adolescent mental health based on big data and machine learning

Abstract

Adolescent mental health issues like depression and anxiety are rising among adolescents worldwide, leading to serious long-term consequences if left untreated. However, stigma and lack of resources delay access to timely screening and care. Traditional subjective methods fail to detect early warning signs and risk factors. This paper develops a novel hybrid convolutional neural network (CNN) and long short-term memory (LSTM) model to enable early warning of common mental health risks like depression in teenagers. The model is trained on a large clinical dataset with over 50,000 adolescents encompassing electronic health records and neuroimaging data. This allows discovering subtle predictive patterns within clinical encounters and brain structure or function at the population level. The methodology centers on a tailored CNN and LSTM architecture to comprehensively model the multivariate clinical time series data, combining CNN spatial feature learning with LSTM temporal sequence modeling. The hybrid approach leverages complementary techniques to analyze the neuroimaging and electronic health record data. The model achieves an accuracy of 95%, AUC of 97%, precision of 94%, recall of 91%, and F1 score of 92% on held-out test data, significantly outperforming prior state-of-the-art models which had lower accuracy. Detailed predictive feature analysis and clinical validation confirm the model's utility for early mental health risk screening and targeted intervention in teenagers. Overall, the study demonstrates an effective approach to integrating big data, deep learning and rigorous evaluation for developing more accurate, explainable and useful models to support adolescent mental healthcare through data-driven early warning systems.