AIMC Journal:
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Showing 1 to 10 of 12 articles

Digital doppelgangers in psychiatry.

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Digital doppelgangers are individualized, continuously updated digital representations of a person constructed from behavioral, physiological, and contextual data streams, including smartphone metadata, wearable sensor outputs, social media activity,...

A bibliometric analysis of global research trends and emerging hotspots on suicide and depression among children and adolescents.

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BACKGROUND: Suicide and depression among children and adolescents represent critical global public health challenges. Despite a rapidly growing body of literature, the overall knowledge structure, research trends, and emerging directions in this fiel...

Associations between anterior hypothalamic subunits and ADHD and autistic traits revealed by deep learning MRI segmentation.

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The hypothalamus is a central regulator of neuroendocrine function and social behavior, yet its internal organization has remained difficult to examine in vivo in human neurodevelopmental research. Based on neuroendocrine models, we hypothesized that...

Integrating epidemiologic modeling and explainable machine learning to predict and identify factors associated with self-reported depression among adults in Tennessee, United States.

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BACKGROUND: Depression is a major public health concern, with Tennessee ranking among the U.S. states with the highest prevalence. Despite its burden, many cases remain undetected due to limited screening and access to mental health services. This st...

A hybrid AIoT model for workplace stress prediction and intervention using social media platforms.

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Nowadays, stress-related issues are more challenging for employees in the workplaces. As attitudes and practices change in the workplace, employees are more likely to feel stressed. Employees are struggling to maintain their work-life balance. Tradit...

Explainable machine learning for mental health prediction from social media behavior: a nested cross-validation study with SHAP and LIME interpretability.

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Social media behavior is a promising source of early indicators for psychological distress; however, predictive models often lack transparency, limiting their adoption in mental health settings. This paper describes an explainable machine learning fr...