Depression

Predicting Suicides Among US Army Soldiers After Leaving Active Service.

IMPORTANCE: The suicide rate of military servicemembers increases sharply after returning to civilian life. Identifying high-risk servicemembers before they leave service could help target preventive interventions.

Detection of suicidality from medical text using privacy-preserving large language models.

BACKGROUND: Attempts to use artificial intelligence (AI) in psychiatric disorders show moderate success, highlighting the potential of incorporating information from clinical assessments to improve the models. This study focuses on using large language models (LLMs) to detect suicide risk from medical text in psychiatric care.

AI-assisted summary of suicide risk Formulation

Background: Formulation, associated with suicide risk assessment, is an individualised process that seeks to understand the idiosyncratic nature and...

Toward molecular diagnosis of major depressive disorder by plasma peptides using a deep learning approach.

Major depressive disorder (MDD) is a severe psychiatric disorder that currently lacks any objective diagnostic markers. Here, we develop a deep learni...

AI-Driven Early Mental Health Screening: Analyzing Selfies of Pregnant Women

Major Depressive Disorder and anxiety disorders affect millions globally, contributing significantly to the burden of mental health issues. Early sc...

Suicide Phenotyping from Clinical Notes in Safety-Net Psychiatric Hospital Using Multi-Label Classification with Pre-Trained Language Models

Accurate identification and categorization of suicidal events can yield better suicide precautions, reducing operational burden, and improving care ...

Large-scale digital phenotyping: identifying depression and anxiety indicators in a general UK population with over 10,000 participants

Digital phenotyping offers a novel and cost-efficient approach for managing depression and anxiety. Previous studies, often limited to small-to-medi...

Analysis of In-Home Movement Patterns for Depression Assessment in Older Adults - A Feasibility Study.

Depression significantly impacts the wellbeing of older Australians, posing considerable challenges to their overall quality of life. This study aimed...

Artificial Intelligence in Military Medicine.

Artificial intelligence (AI) has garnered significant attention for its pivotal role in the national security and health care sectors. However, its ut...

Multilayer Network of Cardiovascular Diseases and Depression via Multipartite Projection

Cardiovascular diseases (CVD) and depression exhibit significant comorbidity, which is highly predictive of poor clinical outcomes. Yet, the underly...

Integrating Large Language Models into a Tri-Modal Architecture for Automated Depression Classification on the DAIC-WOZ

Major Depressive Disorder (MDD) is a pervasive mental health condition that affects 300 million people worldwide. This work presents a novel, BiLSTM...

Machine learning for detection of heterogeneous effects of Medicaid coverage on depression.

In 2008, Oregon expanded its Medicaid program using a lottery, creating a rare opportunity to study the effects of Medicaid coverage using a randomize...

MDDBranchNet: A Deep Learning Model for Detecting Major Depressive Disorder Using ECG Signal.

Major depressive disorder (MDD) is a chronic mental illness which affects people's well-being and is often detected at a later stage of depression wit...

Neural substrates of predicting anhedonia symptoms in major depressive disorder via connectome-based modeling.

MAIN PROBLEM: Anhedonia is a critical diagnostic symptom of major depressive disorder (MDD), being associated with poor prognosis. Understanding the n...

Data Quality Matters: Suicide Intention Detection on Social Media Posts Using RoBERTa-CNN.

Suicide remains a pressing global health concern, necessitating innovative approaches for early detection and intervention. This paper focuses on iden...

TAU-DI Net: A Multi-Scale Convolutional Network Combining Prob-Sparse Attention for EEG-based Depression Identification.

EEG-based detection of major depression disorder (MDD) plays a pivotal role in the subsequent treatment and recovery. With the rapid development of de...

Exploring Self-Supervised Models for Depressive Disorder Detection: A Study on Speech Corpora.

Automatic detection of depressive disorder from speech signals can help improve medical diagnosis reliability. However, a significant challenge in thi...

Diagnosing Suicidal Ideation from Resting State EEG Data Using a Machine Learning Algorithm.

Suicide poses a global health crisis with significant social and economic impact. Prevention may be possible if objective quantitative methods are dev...

A Sentiment Pre-trained Text-Guided Multimodal Cross-Attention Transformer for Improved Depression Detection.

Depression is a widespread mental health issue requiring efficient automated detection methods. Traditional single-modality approaches are less effect...

FacePsy: An Open-Source Affective Mobile Sensing System -- Analyzing Facial Behavior and Head Gesture for Depression Detection in Naturalistic Settings

Depression, a prevalent and complex mental health issue affecting millions worldwide, presents significant challenges for detection and monitoring. ...