Service-Centric Adolescent Mental Health Early Warning Solutions and Enabling Technologies: Survey and Framework

Abstract

The increasing global prevalence of adolescent mental health challenges demands proactive early warning systems. Although such systems have attracted growing research interest, most prior work emphasizes algorithmic performance, while the surrounding service infrastructure remains underexplored. To address key limitations in current practice, including data silos, insufficient multimodal data integration, and inadequate privacy protection during continuous model evolution, this paper presents a service-oriented, end-to-end framework for adolescent mental health early warning, in which data governance, modeling, and feedback are organized as interoperable microservices. The framework adopts a lakehouse architecture as a governed data layer that unifies multimodal schemas, data lineage, access control, and reusable analytics to facilitate cross-institution collaboration. Built upon this layer, we develop multimodal feature representations that integrate textual, visual, auditory, and physiological signals, together with a modular early warning service engine. To support continual refinement without exposing sensitive data, we further propose Dynamic Federated Co-Evolution (DFCE), a paradigm that couples cross-institutional federated learning with an expert-in-the-loop feedback mechanism, allowing models to co-evolve with expert feedback across institutions. As a proof of concept for the DFCE component, experiments on multimodal sentiment benchmarks show that DFCE maintains competitive predictive performance while enabling privacy-preserving collaboration under continual updates. We position these results as evidence of algorithmic and systems-level feasibility for privacy-preserving, cross-institutional collaboration, without claiming clinical diagnostic validity.