Depression

Characteristics of brain network connectome and connectome-based efficacy predictive model in bipolar depression.

Aberrant functional connectivity (FC) between brain networks has been indicated closely associated w...

Can circadian rhythms of heart rate variability identify major depressive disorder? - A study based on support vector machine analysis.

BACKGROUND: Major depressive disorder (MDD) is a prevalent and severe psychiatric condition for whic...

Development and validation of a risk prediction model for depression in patients with chronic obstructive pulmonary disease.

BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) is a prevalent respiratory condition often ...

Crisis-line workers' perspectives on AI in suicide prevention: a qualitative exploration of risk and opportunity.

BACKGROUND: Crisis support services offer crucial intervention for individuals in acute distress, pr...

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

OBJECTIVE: Depression in older adults is a growing public health concern, yet there is still a lack ...

Correction: A multimodal approach to depression diagnosis: insights from machine learning algorithm development in primary care.

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

Anxiety and depression disorders are increasingly common, necessitating methods for real-time assess...

An interpretable machine learning model for predicting depression in middle-aged and elderly cancer patients in China: a study based on the CHARLS cohort.

BACKGROUND: Depression is very common in middle-aged and elderly cancer patients, which will serious...

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.

BACKGROUND: Mild to moderate depression (MMD), as an early stage of depression, has a high incidence...

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

The Forced Swim Test (FST) is a widely used preclinical model for assessing antidepressant efficacy,...

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

Depression is a prevalent mental health disorder, and early detection is crucial for timely interven...

Different prefrontal cortex activity patterns in bipolar and unipolar depression during verbal fluency tasks based on functional near infrared spectroscopy study.

This study aimed to investigate the functionality of the prefrontal cortex in patients with unipolar...

Suicide Risk Screening in Jails: Protocol for a Pilot Study Leveraging the Mental Health Research Network Algorithm and Health Care Data.

BACKGROUND: Suicide in local jails occurs at a higher rate than in the general population, requiring...

Multimodal Depression Recognition via Mutual Information Maximization Joint with Multi-task Learning.

Depression is a serious mental health disorder with a potential hazard for individuals and society c...

Alterations in Gut Microbiota-Brain Axis in Major Depressive Disorder as Identified by Machine Learning.

Major depressive disorder (MDD) is a complex mental health condition whose causes may extend beyond ...

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

This study examined whether baseline demographic and clinical variables could predict clinically si...

Development of the treatment prediction model in the artificial intelligence in depression - medication enhancement study.

We introduce an artificial intelligence model to personalize treatment in major depression, which wa...

Machine Learning Model for Predicting Sertraline-like Activities and Its Impact on Cancer Chemosensitization.

Selective serotonin reuptake inhibitors (SSRIs) like sertraline are crucial in treating depression a...

AI in Medical Questionnaires: Innovations, Diagnosis, and Implications.

This systematic review aimed to explore the current applications, potential benefits, and issues of ...

Leveraging machine learning to uncover the hidden links between trusting behavior and biological markers.

Understanding the decision-making mechanisms underlying trust is essential, particularly for individ...

Applying language models for suicide prevention: evaluating news article adherence to WHO reporting guidelines.

The responsible reporting of suicide in media is crucial for public health, as irresponsible coverag...