AIMC Topic: Depression

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Machine learning-based predictive modeling of depressive symptoms in Chinese adolescents.

Journal of affective disorders
BACKGROUND: The aim is to develop prediction models by lifestyles indicators as well as socioeconomic status to predict the risk of depressive symptoms in adolescents, and to rank and explain these predictors.

Functional Consequences of Tinnitus in Military Service Members.

American journal of audiology
PURPOSE: Numerous individuals in the United States are bothered enough by tinnitus that it affects normal daily activities, including sleep and concentration. There is a high prevalence of self-reported bothersome tinnitus in the U.S. military, and t...

A Conversational Robot for Cognitively Impaired Older People Who Live Alone: An Exploratory Feasibility Study.

Psychogeriatrics : the official journal of the Japanese Psychogeriatric Society
BACKGROUND: Social isolation and loneliness are significant risk factors for poor mental health in older adults, particularly those living alone with cognitive impairment. Socially assistive robots (SARs) offer a promising approach to enhance social ...

Blood immuno-metabolic biomarker signatures of depression and affective symptoms in young adults.

Brain, behavior, and immunity
BACKGROUND: Depression is associated with alterations in immuno-metabolic biomarkers, but it remains unclear whether these alterations are limited to specific markers, and whether there are subtypes of depression and depressive symptoms which are ass...

Predicting depression in healthy young adults: A machine learning approach using longitudinal neuroimaging data.

NeuroImage
Accurate prediction of depressive symptoms in healthy individuals can enable early intervention and reduce both individual and societal costs. This study aimed to develop predictive models for depression in young adults using machine learning (ML) te...

Multimodal depression recognition and analysis: Facial expression and body posture changes via emotional stimuli.

Journal of affective disorders
BACKGROUND: Clinical studies have shown that facial expressions and body posture in depressed patients differ significantly from those of healthy individuals. Combining relevant behavioral features with artificial intelligence technology can effectiv...

Modeling the Determinants of Subjective Well-Being in Schizophrenia.

Schizophrenia bulletin
BACKGROUND: The ultimate goal of successful schizophrenia treatment is not just to alleviate psychotic symptoms, but also to reduce distress and achieve subjective well-being (SWB). We aimed to identify the determinants of SWB and their interrelation...

Incorporating end-user perspectives into the development of a machine learning algorithm for first time perinatal depression prediction.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Machine learning algorithms can advance clinical care, including identifying mental health conditions. These algorithms are often developed without considering the perspectives of the affected populations. This study describes the process ...

Prediction of post stroke depression with machine learning: A national multicenter cohort study.

Journal of psychiatric research
OBJECTIVE: Post-stroke depression (PSD) is a common psychiatric complication following stroke, with low clinical detection rates and delayed diagnosis. Most existing PSD prediction models suffer from incomplete data inclusion, which limits their clin...

Exploring artificial intelligence (AI) Chatbot usage behaviors and their association with mental health outcomes in Chinese university students.

Journal of affective disorders
Technology dependence has long been a critical public health issue, especially among young people. With the development of AI chatbots, many individuals are integrating these tools into their daily lives. However, we have limited knowledge about the ...