Depression

Smartphone Addiction, Use Preferences, and Depression Among Older Adults in the Digital Context: Machine Learning Analysis of Survey Data.

BACKGROUND: The increasing use of smartphones among older adults offers new opportunities for social connection but may also pose risks associated with adverse mental health outcomes, including depression. OBJECTIVE: This study examined the relationship between smartphone use and depression among older adults in Guangzhou, China, to identify key predictors and complex configurations associated wit...

Transient frontal spectral events from EEG predict antidepressant response to sertraline in depression.

Resting-state scalp electroencephalography (EEG) is a promising method for predicting patient outcomes of antidepressant treatments. Machine-learning-based EEG analyses of averaged power features (APF) have predicted antidepressant responders in standalone samples but have not yet significantly impacted clinical care. Here, we applied new approaches for analyzing transient spectral event features ...

Multimodal machine learning for major depressive disorder: Integrating EEG functional connectivity and clinical variables to enhance diagnostic accuracy.

The diagnosis of Major Depressive Disorder (MDD) relies heavily on subjective clinical assessments. This study evaluated various machine learning mode...

Mortality score in chronic coronary syndrome: prediction model from the ISCHEMIA trial.

AIMS: Accurate stratification of mortality risk is essential for management of chronic coronary syndromes (CCS), but existing models focus primarily o...

Construction and validation of a predictive model for suicidal attempts in patients with mood disorders after discharge based on machine learning methods: A two-year follow-up study.

BACKGROUND: Suicide attempts (SA) in patients with mood disorders (MD) should be paid enough attention. Currently, there is a lack of relevant researc...

The Performance of Wearable Device-Based Artificial Intelligence in Detecting Depression: Systematic Review and Meta-Analysis.

BACKGROUND: In recent years, advances in wearable sensor technology and artificial intelligence (AI) have provided new possibilities for detecting and...

Functional brain network modulation following conventional and optimized HD-tDCS in major depressive disorder: A machine learning prediction of treatment response.

BACKGROUND: Major depressive disorder (MDD) is a prevalent and disabling condition that remains inadequately treated in many patients. Transcranial di...

DisNet : Learning interpretable depression representations in speech.

Speech-based depression detection (SDD) offers an objective and convenient method for depression screening and intervention. However, existing deep le...

Deep Brain Stimulation Induces Antidepressant Effects by Restoring High-Fidelity Communication in the BNST-NAc Circuit.

Deep brain stimulation (DBS) for treatment-resistant depression (TRD) is challenged by significant individual variability in efficacy and unclear neur...

Magnetic resonance imaging study of major depressive disorder: a 28-year scientometric and visual analysis.

Neuroimaging, particularly magnetic resonance imaging (MRI), has become a cornerstone in elucidating the neural underpinnings of Major Depressive Diso...

Race/ethnicity, disability, and antenatal depression in the United States: population-level insights from machine learning.

OBJECTIVE: Women with intersecting identities, such as being both Black and disabled, face heightened risk of antenatal depression, yet few studies ex...

Beyond acoustic features: Incorporating linguistic variables in automatic speech analysis for depression detection.

BACKGROUND: Most research on automatic speech analysis (ASA) has focused on acoustic features, while the potential of linguistic markers remains under...

From pharmacokinetic genes to polygenic scores and multi-omic integration: advances towards precision psychiatry.

INTRODUCTION: . Pharmacological treatment is the mainstay in the acute and long-term management of severe mental disorders such as major depressive di...

Neuroimaging insights into the neurophysiological subtypes of major depressive disorder.

Major depressive disorder (MDD) is a highly heterogeneous condition that limits the reliability of symptom-based diagnosis and treatment selection. In...

Classification of suicide attempt history in adolescents with affective disorders: An exploratory machine learning study.

INTRODUCTION: Diagnosis of affective disorders among adolescent population links with the high risk of suicide attempt. The use of clinical psychologi...

Evaluating the potential of applying artificial intelligence in the diagnosis of major depressive disorder: Neuroimaging and clinical behavioral markers.

BACKGROUND: Consensus exists that point-of-care in scalable capabilities are required to improve the timeliness and accuracy of Major Depressive Disor...

Using machine learning to reveal two distinct neuroanatomical subtypes of first-episode, drug-naïve major depressive disorder: Evidence from the REST-meta-MDD project.

BACKGROUND: Major depressive disorder (MDD) is a highly heterogeneous condition, complicating biomarker discovery and precision medicine. Identifying ...

Exploring interactive pathways to infant disorganized attachment in a sample of mothers with postpartum depression.

Research on interactions between child and environmental factors in the development of infant disorganized attachment is relatively limited. Using pre...

Real-World Testing of an Artificial Intelligence Conversational Agent as an Early Intervention and Support Tool in the Mental Health Referral Care Pathway: A Mixed-Methods Study.

BACKGROUND: The incidence of mental health concerns is growing, and demand for support is exceeding service capacity. Digital tools can provide additi...

Comparing Risk Prediction for Suicide Attempts and Deaths After Emergency Department Visits for Individuals With Mental Health Disorders.

Objective: To identify and compare predictors of nonfatal and fatal suicidal events within 180 days of emergency department (ED) visits for mental hea...