Optima Analytica @Optimaanalytica

MCoG-LDPSNet represents a significant advancement in AI-powered mental health screening, by combining novel brain-inspired architecture with empirically validated real-world efficacy [1]. Its robust approach to severe data imbalance and consistently high predictive reliability…

Are cognitive models the future of mental health? Our Biomimetics paper introduces MCoG-LDPSNet, a novel neuroscience-informed model for mobile mental health early screening of anxiety and depression [1]. Orthogonal, recurrent, modular, parallel preprocessing pipelines feed two…

MCoG-LDPSNet: A novel brain-inspired model with dual orthogonal encoders and a new Loss-Driven Parametric Swish that adaptively amplifies minority-class signals and improves calibration under severe imbalance. Benchmark: AUROC 0.9920 | G-mean 0.9451 | transfer AUROC 0.9937.…

New brain-inspired model MCoG-LDPSNet reduces AI bias from severe class imbalance in mobile mental health screening with near-perfect AUROC (~0.9937) and linked to big symptom drops in EmotiZen users (anxiety −28.2%, depression −42.1%). doi.org/10.3390/biomim… #MentalHealth…

Access to care matters. EmotiZen presents the MCoG-LDPSNet, a novel brain-inspired model that better detects rare but critical mental health signals in text so screening is fairer and more reliable. Results: AUROC 0.9920 | transfer 0.9937. Real world impact: anxiety -28.2%…

EmotiZen unveils MCoG-LDPSNet. A powerful, novel brain-inspired AI model with dual orthogonal encoders and a new Loss-Driven Parametric Swish that amplifies minority class signals and improves calibration under severe imbalance. Benchmark: AUROC 0.9920 | G-mean 0.9451 | transfer…

Stigma & waitlists block care. A new AI Brain-Inspired model, MCoG-LDPSNet, powers the EmotiZen App to screen anxiety & depression reliably, even with imbalanced data, achieving near-perfect accuracy and tied to 28% less anxiety & 42% less depression. doi.org/10.3390/biomim……

New brain-inspired model MCoG-LDPSNet reduces AI bias from severe class imbalance in mobile mental-health screening with near-perfect AUROC (~0.994) and linked to big symptom drops in @EmotiZen_AI users (anxiety −28.2%, depression −42.1%). doi.org/10.3390/biomim… #MentalHealth

EmotiZen unveils MCoG-LDPSNet [1]. A powerful brain-inspired model with dual orthogonal encoders and a novel Loss-Driven Parametric Swish layer that adaptively amplifies minority class signals and improves calibration under severe imbalance, outperforming SOTA machine and deep…

Integrating Brain-Inspired AI Models into Preventive Healthcare Preventive healthcare is most effective when it looks beyond single risk factors and addresses the complete picture of a person’s health. While genetics and lifestyle remain significant, psychological symptoms,…

Mobile Mental Health Screening in EmotiZen via the Novel Brain-Inspired MCoG-LDPSNet Our new publication in @Biomim_MDPI presents MCoG-LDPSNet, a novel brain-inspired approach to mobile mental health screening [1]. The proposed MCoG-LDPSNet uses two separate pathways to learn…

Mobile Mental Health Screening in EmotiZen via the Novel Brain-Inspired MCoG-LDPSNet Dear community, we all know that anxiety and depression affect millions, yet stigma and long wait times keep many from care. Mobile apps and platforms can reduce those barriers, provided their…

Mobile Mental Health Screening in EmotiZen via the Novel Brain-Inspired MCoG-LDPSNet Our study introduces MCoG-LDPSNet, a novel brain-inspired predictive mental health algorithm that achieves world-class performance. The proposed MCoG-LDPSNet outperforms both traditional and…

AI-Powered Early Detection of Anxiety, Depression, and ADHD: A Human-Centered Digital Health Approach Neurodevelopmental disorders such as Attention-deficit/hyperactivity disorder (ADHD) frequently present alongside internalizing conditions like anxiety and depression, which can…

Harnessing the Early Signals of Anxiety and Depression to Anticipate Multiple Sclerosis: EmotiZen Health’s Proactive Approach Recent scientific evidence demonstrates that anxiety and depression often emerge before Multiple Sclerosis (MS) is formally diagnosed, and may serve as…

From Feedback Loops to Wellbeing: Ethical AI in Neurofinance and Mental Health Inspired by Norbert Wiener’s pioneering work in cybernetics, our Artificial Intelligence (AI) and computational neuroscience research embraces the view that intelligence emerges from adaptive feedback…

Monitoring Mental Health Risks Early with Brain-Inspired AI Artificial Intelligence (AI) deep learning models that take cues directly from neural circuits in an effort to mimic how neurons communicate, adapt, and reorganize show that cognitive algorithms could discover patterns…

Early Mental Health Detection and Brain-Inspired Innovation by emotizen.health The synergy between neuroscience and Artificial Intelligence (AI) is redefining the field of mental health care. As an AI and computational neuroscience researcher, I explore this dynamic…

The integration of emotizen.health Artificial Intelligence (AI) cutting-edge algorithms into early mental health prognosis has set new benchmarks in the field. These same algorithms have not only been recognized with awards but have also demonstrated superior performance…