Mental health early warning for college students by integrating multi-source data and TSEN algorithm

Abstract Regarding the problems of strong lag in traditional questionnaires and limited representation of single source data in monitoring the mental health of college students, a mental health warning method is proposed by integrating multi-source campus behavior data with the Temporal-Behavioral Stream Encoding Network (TSEN). By integrating four types of data including academic performance, daily life behavior, online behavior, and psychological profiles (covering 15,682 students from 10 colleges and 4 grades), a Random Walk-based Kalman model is used to fill in missing temporal values. A high-quality dataset is constructed by combining Bidirectional Encoder Representation from Transformers (BERT) text completion and isolated forest anomaly detection based on Transformers. Then, the TSEN dual stream coding framework is designed to achieve early warning of college students’ mental health. Experiments on the public dataset StudentLife and the real campus dataset CAMP showed that the F1 values of TSEN reached 92.58% and 83.29%, which are 2.94% and 2.15% higher than the optimal baseline. The parameter size was only 6.7 M and the inference delay was ≤ 38 ms. In actual deployment, the college student mental health warning model based on the TSEN algorithm identified high-risk psychological problems 6.2 weeks earlier, with a diagnosis rate of 89.76%. This research result can provide high-precision and low invasive early warning solutions for college students’ mental health problems.