AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Depression

Showing 41 to 50 of 351 articles

Clear Filters

Functional Disability and Psychological Impact in Headache Patients: A Comparative Study Using Conventional Statistics and Machine Learning Analysis.

Medicina (Kaunas, Lithuania)
: Recent research has focused on exploring the relationships between various factors associated with headaches and understanding their impact on individuals' psychological states. Utilizing statistical methods and machine learning models, these studi...

Finding purpose: Integrated latent profile and machine learning analyses identify purpose in life as an important predictor of high-functioning recovery after alcohol treatment.

Addictive behaviors
BACKGROUND: Recent investigations of recovery from alcohol use disorder (AUD) have distinguished subgroups of high and low functioning recovery in data from randomized controlled trials of behavioral treatments for AUD. Analyses considered various in...

The predictive role of sedentary behavior and physical activity on adolescent depressive symptoms: A machine learning approach.

Journal of affective disorders
OBJECTIVE: This study aims to investigate the predictive value of sedentary behavior and physical activity in adolescent depressive symptoms.

Optimizing depression detection in clinical doctor-patient interviews using a multi-instance learning framework.

Scientific reports
In recent years, the number of people suffering from depression has gradually increased, and early detection is of great significance for the well-being of the public. However, the current methods for detecting depression are relatively limited, typi...

A machine-learning-derived online prediction model for depression risk in COPD patients: A retrospective cohort study from CHARLS.

Journal of affective disorders
BACKGROUND: Depression associated with Chronic Obstructive Pulmonary Disease (COPD) is a detrimental complication that significantly impairs patients' quality of life. This study aims to develop an online predictive model to estimate the risk of depr...

Fast, smart, and adaptive: using machine learning to optimize mental health assessment and monitor change over time.

Scientific reports
In mental health, accurate symptom assessment and precise measurement of patient conditions are crucial for clinical decision-making and effective treatment planning. Traditional assessment methods can be burdensome, especially for vulnerable populat...

[Prediction of depression symptoms in seniors and analysis of influencing factors based on explainable machine learning].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi
This study aims to construct a machine learning model to predict depression symptoms in the elderly and analyze the key influencing factors of depression in the elderly using the shapley additive interpretation (SHAP) method. Based on entries from ...

Efficacy and effectiveness of therapist-guided internet versus face-to-face cognitive behavioural therapy for depression via counterfactual inference using naturalistic registers and machine learning in Finland: a retrospective cohort study.

The lancet. Psychiatry
BACKGROUND: According to meta-analyses of randomised controlled trials (RCTs), therapist-guided internet-delivered cognitive behavioural therapy (iCBT) is as effective a treatment for depression as traditional face-to-face CBT (fCBT), despite its sub...

Comparison of logistic regression and machine learning methods for predicting depression risks among disabled elderly individuals: results from the China Health and Retirement Longitudinal Study.

BMC psychiatry
BACKGROUND: Given the accelerated aging population in China, the number of disabled elderly individuals is increasing, and depression is a common mental disorder among older adults. This study aims to establish an effective model for predicting depre...

WavFace: A Multimodal Transformer-Based Model for Depression Screening.

IEEE journal of biomedical and health informatics
Depression, a prevalent mental health disorder with severe health and economic consequences, can be costly and difficult to detect. To alleviate this burden, recent research has been exploring the depression screening capabilities of deep learning (D...