Depression

Predicting depression severity among people living with HIV in Oman using random forest.

Depression is the most common psychiatric comorbidity among people living with HIV and is associated with an increased risk of disease progression. However, existing screening tools generally do not account for sociodemographic and psychosocial factors. Machine learning models offer a promising approach by capturing complex interactions in variables that traditional methods overlook. This cross-se...

Pharmacologic and expectancy effects in depression: Associations with inter-network resting-state connectivity.

Depression involves dysregulation across large-scale neural networks, yet substantial heterogeneity in resting-state functional connectivity (rsFC) across patients limits our understanding of treatment mechanisms. A key unresolved question is whether baseline network architecture differentially predicts response to pharmacological versus expectancy-driven treatment effects. In this mechanistic, hy...

Design and optimization of deep learning model based on multimodal data fusion for dynamic mental health assessment.

The dynamic assessment of mental health has emerged as a hotspot for study and application due to the rise in social pressure. However, onventional me...

Attention-Enhanced Static-Dynamic Fusion Network for Major Depressive Disorder Diagnosis.

OBJECTIVE: Accurate depression classification using fNIRS signals is critical for objective auxiliary diagnosis, yet many existing methods separately ...

Machine learning-enabled behavioural and psychological symptoms of dementia management intervention for dementia caregivers: protocol for a hybrid factorial SMART-MRT trial.

BACKGROUND: Behavioural and psychological symptoms of dementia (BPSD) affect over 90% of people living with dementia and are a major contributor to st...

Unifying the hallmarks of major depression through neuroimmune-metabolic-oxidative (NIMETOX) dysregulation: a mechanistic systems framework.

Current state-of-the-art neuroimmune, metabolic, and oxidative stress (NIMETOX) knowledge that has been developed in clinical major depressive disorde...

Frequency-decomposed EEG microstate analysis reveals altered cross-frequency coordination in adolescent major depressive disorder.

Adolescent major depressive disorder (MDD) involves alterations in large‑scale brain network dynamics. However, conventional EEG microstate studies ty...

Phenotyping antidepressant treatment response with deep learning in electronic health records.

Efficient, accurate phenotyping for antidepressant treatment response in electronic health records (EHRs) could facilitate precision psychiatry applic...

From hype to reality: the feasibility, dilemmas, and solutions of Gen AI in medical education from students' perspectives.

BACKGROUND: The rapid evolution of generative artificial intelligence (AI) has sparked a pedagogical debate over whether AI can replace human teachers...

Brain network correlates of fatigue, depression, and anxiety in patients with Crohn's Disease in different disease states.

BACKGROUND: Symptoms of fatigue, depression or anxiety are frequent in Crohn's Disease (CD) and may relate to disturbed brain-gut interactions. While ...

Can speech reveal mental health status? A study of linguistic features across multiple samples of at-risk adults.

An urgent challenge in clinical science is the reliable detection of psychiatric risk in the aftermath of traumatic or stressful events. Current scree...

A bibliometric analysis of global research trends and emerging hotspots on suicide and depression among children and adolescents.

BACKGROUND: Suicide and depression among children and adolescents represent critical global public health challenges. Despite a rapidly growing body o...

DepressLLM: Interpretable Domain-Adapted Language Model for Depression Detection from Real-World Narratives.

Advances in large language models (LLMs) have enabled a wide range of applications. However, depression prediction is hindered by the lack of large-sc...

Artificial intelligence and machine learning for detecting suicidal ideation and related suicide outcomes among adolescents: A scoping review.

OBJECTIVE: This scoping review aimed to map the existing evidence on the application of artificial intelligence and machine learning in detecting suic...

Machine learning model based on spontaneous brain activity detected by functional MRI for distinguishing unipolar depression from bipolar disorder.

BACKGROUND: The therapeutic strategies for bipolar disorder (BD) and unipolar depression (UD) are quite different. However, the majority of patients w...

Machine learning-driven correction of handgrip strength: a novel biomarker for neurological and health outcomes in the UK Biobank.

Handgrip strength (HGS) is a significant biomarker for overall health, offering a simple, cost-effective method for assessing muscle function. Lower H...

Metaheuristic optimized hybrid machine learning framework for predicting soil compaction parameters.

Accurate prediction of maximum dry density (MDD) and optimum moisture content (OMC) is critical for effective compaction control and earthwork design ...

Bioinformatics analysis reveals the characteristics of immune microenvironment in major depressive disorder and vitiligo.

BACKGROUND: Major depressive disorder (MDD) and vitiligo often occur together, worsening patient outcomes. However, the shared pathogenic mechanisms r...

Capturing omega sign in the clinical assessment of depression by deep learning.

The omega sign is a melancholic facial expression, resembling the Greek letter omega "Ω". To study static omega sign (visible full-blown Ω-shaped or v...

Suicide risk prediction and analysis of associated factors among high school students in four provinces of China based on explainable machine learning.

BACKGROUND: Suicidal ideation (SI) among adolescents is a severe global public health issue; however, accurate identification and mechanistic explanat...