AIMC Topic: Depression
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The interpretable machine learning model for depression associated with heavy metals via EMR mining method.
Scientific reports
Mar 28, 2025
Limited research exists on the association between depression and heavy metal exposure. This study aims to develop an interpretable and efficient machine learning (ML) model with robust performance to identify depression linked to heavy metal exposur...
Machine learning-driven development of a stratified CES-D screening system: optimizing depression assessment through adaptive item selection.
BMC psychiatry
Mar 26, 2025
OBJECTIVE: To develop a stratified screening tool through machine learning approaches for the Center for Epidemiologic Studies Depression Scale (CES-D-20) while maintaining diagnostic accuracy, addressing the efficiency limitations in large-scale app...
An interpretable deep-learning approach to detect biomarkers in anxious-depressed symptoms from prefrontal fNIRS signals during an autobiographical memory test.
Asian journal of psychiatry
Mar 22, 2025
BACKGROUND: Individuals with anxious-depressed (AD) symptoms have more severe mood disorders and cognitive impairment than those with non-anxious depression (NAD) symptoms. Therefore, it is important to clarify the underlying neurophysiology of these...
Network-based predictive models for artificial intelligence: an interpretable application of machine learning techniques in the assessment of depression in stroke patients.
BMC geriatrics
Mar 22, 2025
BACKGROUND: Depression is a common complication after a stroke that may lead to increased disability and decreased quality of life. The objective of this study was to develop and validate an interpretable predictive model to assess the risk of depres...
Utilizing machine learning algorithms for predicting Anxiety-Depression Comorbidity Syndrome in Gastroenterology Inpatients (ADCS-GI).
BMC psychiatry
Mar 18, 2025
BACKGROUND: Accurately diagnosing Anxiety-Depression Comorbidity Syndrome in Gastroenterology Inpatients (ADCS-GI) shows significant challenges as traditional diagnostic methods fail to meet expectations due to patient hesitance and non-psychiatric h...
Using machine learning to predict depression among middle-aged and elderly population in China and conducting empirical analysis.
PloS one
Mar 18, 2025
OBJECTIVE: To develop a predictive model for evaluating depression among middle-aged and elderly individuals in China.
Utilizing machine learning to identify fall predictors in India's aging population: findings from the LASI.
BMC geriatrics
Mar 17, 2025
BACKGROUND: Depression has a detrimental effect on an individual's mental and musculoskeletal strength multiplying the risk of fall incidents. The current study aims to investigate the association between depression and falls in older adults using ma...
Develop and validate machine learning models to predict the risk of depressive symptoms in older adults with cognitive impairment.
BMC psychiatry
Mar 11, 2025
BACKGROUND: Cognitive impairment and depressive symptoms are prevalent and closely interrelated mental health issues in the elderly. Traditional methods for identifying depressive symptoms in this population often lack effectiveness. Machine learning...
Continuous nursing symptom management in cancer chemotherapy patients using deep learning.
Scientific reports
Mar 7, 2025
To assess the efficacy of a deep learning platform for managing symptoms in chemotherapy patients, aiming to enhance their quality of life. A non-randomized controlled trial was conducted from September 2022 to March 2024, involving 144 chemotherapy ...
Comparison of an AI Chatbot With a Nurse Hotline in Reducing Anxiety and Depression Levels in the General Population: Pilot Randomized Controlled Trial.
JMIR human factors
Mar 6, 2025
BACKGROUND: Artificial intelligence (AI) chatbots have been customized to deliver on-demand support for people with mental health problems. However, the effectiveness of AI chatbots in tackling mental health problems among the general public in Hong ...
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