Explainable Hybrid Machine Learning for Mental Health Outcomes: Revealing Latent Patterns in Patient Data

Abstract

Mental health data is complex, multimodal, and grows by the terabyte each day. Clinicians require algorithms that can filter out noise and provide clear, concise explanations. Hybrid machine-learning frameworks have begun to close this interpretability gap by constraining or guiding data-driven models with clinical insight. Prior reviews emphasized performance; few mapped how explainable, hybrid designs convert latent digital patterns into actionable clinical signals. We surveyed peer-reviewed studies published between 2015 and 2025 that paired explanatory tools (e.g., SHAP, rule lists) with conventional classifiers or neural networks. Most hybrids achieved parity with black-box models in terms of accuracy, while also providing feature-level rationales that clinicians found trustworthy in small usability trials. Smartphone passively sensed behavior and multimodal EHR excerpts yielded the richest “latent patterns,” flagging relapse risk up to two weeks earlier than standard scales. Yet sample heterogeneity, tiny validation cohorts, and sparse reporting of explanation quality remain obstacles. This review maps the emerging design space and highlights the pragmatic trade-offs, accuracy, transparency, and workflow fit that will matter most as hybrid AI moves from proof-of-concept to clinical routine.