Depression

Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model.

BACKGROUND: Prior research has associated spoken language use with depression, yet studies often inv...

A machine learning based depression screening framework using temporal domain features of the electroencephalography signals.

Depression is a serious mental health disorder affecting millions of individuals worldwide. Timely a...

Personality traits as predictors of depression across the lifespan.

BACKGROUND: Depression is a major public health concern. A barrier for research has been the heterog...

Analysis and evaluation of explainable artificial intelligence on suicide risk assessment.

This study explores the effectiveness of Explainable Artificial Intelligence (XAI) for predicting su...

Exploring the promises and pitfalls of artificial intelligence interventions in predicting adolescent self-harm and suicide attempts.

Explainable multimodal prediction of treatment-resistance in patients with depression leveraging brain morphometry and natural language processing.

Although 20 % of patients with depression receiving treatment do not achieve remission, predicting t...

Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model.

Postpartum Depression Disorder (PPDD) is a prevalent mental health condition and results in severe d...

Prediction of suicidal ideation among preadolescent children with machine learning models: A longitudinal study.

BACKGROUND: Machine learning (ML) has been widely used to predict suicidal ideation (SI) in adolesce...

Prediction of treatment response in major depressive disorder using a hybrid of convolutional recurrent deep neural networks and effective connectivity based on EEG signal.

In this study, we have developed a novel method based on deep learning and brain effective connectiv...

DiffMDD: A Diffusion-Based Deep Learning Framework for MDD Diagnosis Using EEG.

Major Depression Disorder (MDD) is a common yet destructive mental disorder that affects millions of...

Predicting suicide risk in real-time crisis hotline chats integrating machine learning with psychological factors: Exploring the black box.

BACKGROUND: This study addresses the suicide risk predicting challenge by exploring the predictive a...

Monitoring Mental Health: Legal and Ethical Considerations of Using Artificial Intelligence in Psychiatric Wards.

Artificial intelligence (AI) is being tested and deployed in major hospitals to monitor patients, le...

Large Language Models and Healthcare Alliance: Potential and Challenges of Two Representative Use Cases.

Large language models (LLMS) emerge as the most promising Natural Language Processing approach for c...

Conversational assessment using artificial intelligence is as clinically useful as depression scales and preferred by users.

BACKGROUND: Depression is prevalent, chronic, and burdensome. Due to limited screening access, depre...

Exploring the Potential of Artificial Intelligence in Adolescent Suicide Prevention: Current Applications, Challenges, and Future Directions.

ObjectiveThe global surge in adolescent suicide necessitates the development of innovative and effic...

Evidence for the biopsychosocial model of suicide: a review of whole person modeling studies using machine learning.

BACKGROUND: Traditional approaches to modeling suicide-related thoughts and behaviors focus on few d...

The Three-Lead EEG Sensor: Introducing an EEG-Assisted Depression Diagnosis System Based on Ant Lion Optimization.

For depression diagnosis, traditional methods such as interviews and clinical scales have been widel...

Assessing prognosis in depression: comparing perspectives of AI models, mental health professionals and the general public.

BACKGROUND: Artificial intelligence (AI) has rapidly permeated various sectors, including healthcare...

A machine learning model to predict the risk of depression in US adults with obstructive sleep apnea hypopnea syndrome: a cross-sectional study.

OBJECTIVE: Depression is very common and harmful in patients with obstructive sleep apnea hypopnea s...

Control list of high-priority chemicals based on 5-HT-RI functionality and the human health interference effects selective CNN-GRU deep learning model.

The antidepressant drug known as 5-HT reuptake inhibitor (5-HT-RI) was commonly detected in biologic...

Development of depression detection algorithm using text scripts of routine psychiatric interview.

BACKGROUND: A psychiatric interview is one of the important procedures in diagnosing psychiatric dis...