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Metaheuristic-optimized cross-modal attention networks for multimodal mental health assessment in college students
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DOI：10.3389/fnsys.2026.1847214.png)
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Abstract
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The increase in mental health disorders in college populations necessitates novel assessment strategies that circumvent the limitations of existing self-report instruments. To address this issue; this paper presents a new deep learning framework for mental health monitoring in academia by integrating multimodal passive sensing data collected from smartphones. A cross-modal attention network; searched by a new metaheuristic algorithm the Addax Optimization Algorithm for neural architecture search; was trained and first evaluated on the StudentLife dataset. To further validate the results due to the extremely limited number of samples in the test set (N = 7); we subsequently performed a zero-shot transfer and fine-tuning evaluation on the College Experience Study (CES) dataset. CES is a large longitudinal dataset of over 200 students collected over 5 years including passively-collected sensor; survey and brain-imaging data. The validation of the model in 140 local participants resulted in classification accuracy for depression of 87.4% on StudentLife (with adjusted 95% CI 62–98%); suggesting considerable uncertainty. On CES the performance of the zero-shot transferred model reached 72.3% of classification accuracy and 83.9% when fine-tuned. This means the initial 87.4% result can be interpreted as overoptimistic. Modality weight analysis showed the importance of survey in predicting depression and the effect of activity on stress predictions. This paper serves as a proof-of-concept of a novel system for screening mental health disorders passively using mobile phones within academia; and suggests it may have a role to play in the early detection of risk.
Keywords:
deep learning
attention network
cross-modal
college student
mental health assessment
Novel Addax optimization algorithm
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