AIMC Topic: Depression

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Interpretable machine learning for depression recognition with spatiotemporal gait features among older adults: a cross-sectional study in Xiamen, China.

BMC geriatrics
OBJECTIVE: Depression in older adults is a growing public health concern, yet there is still a lack of convenient and real-time methods for depressive symptoms identification. This study aims to develop a gait-based depression recognition method for ...

Recognition of anxiety and depression using gait data recorded by the kinect sensor: a machine learning approach with data augmentation.

Scientific reports
Anxiety and depression disorders are increasingly common, necessitating methods for real-time assessment and early identification. This study investigates gait analysis as a potential indicator of mental health, using the Microsoft Kinect sensor to c...

Plasma Lyso-PE 22:6 and Lyso-PE 20:4 are associated with development of mild to moderate depression revealed by metabolomics: a pilot study.

BMC psychiatry
BACKGROUND: Mild to moderate depression (MMD), as an early stage of depression, has a high incidence and may progress to severe depression, even leading to suicide. The lack of effective screening and treatment is due to the unknown metabolic changes...

Machine learning-based model for behavioural analysis in rodents applied to the forced swim test.

Scientific reports
The Forced Swim Test (FST) is a widely used preclinical model for assessing antidepressant efficacy, studying stress response, and evaluating depressive-like behaviours in rodents. Over the last 10 years, more than 5500 scientific articles reporting ...

Depression detection methods based on multimodal fusion of voice and text.

Scientific reports
Depression is a prevalent mental health disorder, and early detection is crucial for timely intervention. Traditional diagnostics often rely on subjective judgments, leading to variability and inefficiency. This study proposes a fusion model for auto...

Sleep disturbances and PTSD: identifying baseline predictors of insomnia response in an intensive treatment programme.

European journal of psychotraumatology
This study examined whether baseline demographic and clinical variables could predict clinically significant reductions in insomnia symptoms among veterans receiving a 2-week Cognitive Processing Therapy (CPT)-based intensive PTSD treatment programm...

Smartphone eye-tracking with deep learning: Data quality and field testing.

Behavior research methods
Eye-tracking is widely used to measure human attention in research, commercial, and clinical applications. With the rapid advancements in artificial intelligence and mobile computing, deep learning algorithms for computer vision-based eye tracking ha...

Development and external validation of an interpretable machine learning model for predicting perinatal depression in Chinese women during mid- and late pregnancy.

International journal of medical informatics
OBJECTIVE: This study aimed to develop a machine learning (ML)-based prediction model for antenatal depression (AND) in Chinese women. Given the significant impact of AND on maternal and infant health, the goal was to create an accurate and interpret...

Prediction of depression risk in middle-aged and elderly Cardiovascular-Kidney-Metabolic syndrome patients by social and environmental determinants of health: an interpretable machine learning approach using longitudinal data from China.

Journal of health, population, and nutrition
BACKGROUND: Cardiovascular-Kidney-Metabolic (CKM) syndrome is a systemic disease characterized by pathophysiological interactions between the cardiovascular system, chronic kidney disease, and metabolic risk factors. In China, the prevalence of CKM i...