Digital Biomarkers and Computational Inference in Smart Neuropsychiatric Healthcare

Abstract

Digital technologies are increasingly integrated into smart healthcare systems, enabling new forms of data-driven assessment and clinical decision support in neuropsychiatric practice. Digital biomarkers derived from behavioral, cognitive, and physiological signals collected through mobile devices, wearable sensors, and digital platforms provide continuous and ecologically valid indicators of symptom dynamics. At the same time, computational inference methods, including statistical modeling and machine learning, support the integration and interpretation of these heterogeneous data sources within clinical workflows. This paper examines how digital biomarkers and computational inference can be integrated into smart neuropsychiatric healthcare systems to enhance assessment, monitoring, and clinical decision-making. Rather than viewing artificial intelligence as an autonomous diagnostic tool, the analysis conceptualizes it as a set of computational mechanisms that transform raw digital signals into structured clinical insights. A three-component framework is proposed, consisting of biomarker extraction, inferential modeling, and clinical decision support integration. Biomarker extraction identifies clinically relevant digital features from real-world data streams. Inferential modeling synthesizes multimodal biomarkers to characterize symptom profiles and predict risk trajectories. Clinical decision support systems embed these inferences within healthcare workflows to assist clinicians in triage, monitoring, and personalized care planning. By situating digital biomarkers and computational inference within the broader context of smart healthcare infrastructures, this framework clarifies how artificial intelligence contributes to scalable, data-driven, and patient-centered neuropsychiatric assessment. The study highlights practical implications for integrating digital phenotyping into electronic health records, telemedicine platforms, and remote monitoring systems, thereby advancing the development of intelligent and adaptive mental healthcare environments.

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