Depression

Early detection of mental health on social media using a hybrid Bi-LSTM-XGBoost model: a comparative study.

The case of mental health disorders has been a main topic in the clinical and psychological field. The advancement of computing studies, especially in Natural Language Processing (NLP)-a subset of Machine Learning, created a system of detection that can detect the mental health state of a person in early stage to prevent the eventuality of the worst case. This is crucial since there has been a lot...

Increased Antidepressant Use after Intensive Care for Aneurysmal Subarachnoid Haemorrhage: A Machine-Learning-Enhanced Propensity-Matched Nationwide Cohort Study.

BACKGROUND: Mood disorders after aneurysmal subarachnoid haemorrhage (aSAH) are common. Meanwhile, mood disorders are also common after intensive care for any reason, and whether aSAH confers an excess risk remains unknown. METHODS: In this retrospective study, patients with cases of aSAH admitted to intensive care in 2012-2023 were identified in nationwide registers and antidepressant (AD) prescr...

Classifying and visualizing medication use in the ABCD study.

Medication use during adolescence provides important insight into current health and treatment patterns. However, these data are often difficult to an...

Development and validation of a machine learning-based screening tool for early detection of adolescent suicide risk.

BACKGROUND: Adolescent suicide remains a significant public health concern, yet existing suicide screening instruments primarily focus on already mani...

Predictive analysis of health-related quality of life trajectories in older patients with chronic pain based on explainable machine learning models.

BACKGROUND: Health-related quality of life (HRQoL) is a vital indicator of evaluating care outcomes and prognosis, yet little is understood about its ...

Beyond Linear Risk: A Machine Learning Approach to Understanding Perinatal Depression in Context.

The goal of this study was to investigate the contextual nature of prenatal depression (PND) and postpartum depression (PPD). We report an investigati...

Applicable Scenarios, Desired Features, and Risks of AI Psychotherapists in Depression Treatment From the Patient's Perspective: Exploratory Qualitative Study.

BACKGROUND: Depression is a pervasive global mental health issue, yet access to trained professionals remains severely limited. With the rapid advance...

Effects of Different Exercise-Led Strategies on Mental, Nutritional, Physical and Cognitive Health in Community-Dwelling Older Adults: A Quasi-Experimental Study.

INTRODUCTION: Exercise interventions are widely used to promote physical and psychosocial health in community-dwelling older adults; however, the comp...

Predicting suicidal ideation in academic communities using machine learning methods: a cross-sectional study.

BACKGROUND: Research consistently shows that depression and suicidal ideation (SI) often cooccur. However, SI can arise without elevated depressive sy...

The applications of single-cell and spatial transcriptomics in neuroscience and brain disorders.

Spatial transcriptomics extends traditional transcriptomic methods by quantifying gene expression within intact tissues while preserving each cell's p...

Attention-based Multimodal Spatiotemporal Enhanced Interaction Network For Major Depressive Disorder Detection.

Although deep learning models have shown promising results in detecting major depressive disorder (MDD), two main limitations remain: insufficient exp...

Evaluating model generalizability for suicide attempt risk prediction: traditional machine vs deep learning.

Suicide remains a leading cause of death and a significant public health concern in the United States. A majority (83%) of suicide decedents had a hea...

Psychiatric Risk Associated With Large Language Model Chatbots: An Emerging Concern.

Large language model chatbots such as ChatGPT have been adopted by millions of users, yet their psychiatric implications are poorly understood. Recent...

Errors in AI-Transformed Patient-Centered Mental Health Documentation Written by Psychiatrists: Qualitative Pre-Post Study.

BACKGROUND: Patients' digital access to their personal health data is becoming increasingly common worldwide. However, medical documentation often con...

The impact of childhood trauma on brain structural connectivity in adolescent major depressive disorder: A graph-based analysis.

BACKGROUND: Childhood trauma (CT) significantly increases vulnerability to adolescent major depressive disorder (MDD), yet the underlying neurobiologi...

Machine learning reveals two neuroanatomical subtypes of adolescent depression with distinct white matter, functional, and cognitive profiles.

BACKGROUND: Major depressive disorder (MDD) is a clinically and neurobiologically heterogeneous disorder typically emerging in adolescence. Delineatin...

Hyper-scanning and hyper-pursuit define eye movement biomarkers of anxiety disorders.

BACKGROUND: Anxiety disorders are highly prevalent yet lack objective biomarkers. Whereas threat-related attentional biases are well documented, less ...

A decade of evidence linking gut microbiome dysbiosis to depression: a computational meta-analysis of mechanistic pathways: 2014-2024.

OBJECTIVES: The gut microbiome-gut-brain axis (MGBA) has been associated in the pathophysiology of depression; however, the expanding literature remai...

Multimodal Depression Detection Through Conversational Interactions with an Emotion-Aware Social Robot: Pilot Study.

BACKGROUND: Depression affects more than 300 million people worldwide and is a leading contributor to the global disease burden. Traditional diagnosti...

Early postpartum psychosocial profiles predict parenting maladjustment at 1 year.

Parenting difficulties during the first postpartum year are a public health concern. Early screening mainly uses depressive-symptom measures such as t...