#MCoGLDPSNet shows how biomimetic model design together with loss-aware activation dynamics can deliver sensitive, validated, trustworthy, and mobile-first screening tools [1]. Crucially, this work is framed for precision medicine because producing calibrated, individual-level risk estimates that integrate with clinical workflows and longitudinal data supports more personalized detection, monitoring, and timely intervention.
Against 18 conventional and state-of-the-art models, MCoG-LDPSNet reached AUROC of 0.9920 and G-mean of 0.9451, and it retained AUROC of 0.9937 after transfer learning. In a 12-week EmotiZen Health rollout the use of the system was associated with a 28.2% reduction in anxiety symptoms and a 42.1% reduction in depressive symptoms, indicating strong potential for earlier, accurate, and scalable screening plus targeted support.
[1]
doi.org/10.3390/biomimetics1…
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