Depression

Resampling to address inequities in predictive modeling of suicide deaths.

OBJECTIVE: Improve methodology for equitable suicide death prediction when using sensitive predictor...

Death by Robots? Automation and Working-Age Mortality in the United States.

The decline of manufacturing employment is frequently invoked as a key cause of worsening U.S. popul...

Translating promise into practice: a review of machine learning in suicide research and prevention.

In ever more pressured health-care systems, technological solutions offering scalability of care and...

Major depression disorder diagnosis and analysis based on structural magnetic resonance imaging and deep learning.

Major depression disorder is one of the diseases with the highest rate of disability and morbidity a...

Designing Depression Screening Chatbots.

Advances in voice recognition, natural language processing, and artificial intelligence have led to ...

Predicting Sex-Specific Nonfatal Suicide Attempt Risk Using Machine Learning and Data From Danish National Registries.

Suicide attempts are a leading cause of injury globally. Accurate prediction of suicide attempts mig...

Deep 3D-CNN for Depression Diagnosis with Facial Video Recording of Self-Rating Depression Scale Questionnaire.

The Self-Rating Depression Scale (SDS) questionnaire is commonly utilized for effective depression p...

The quantification and clinical analysis of depression and anxiety in patients undergoing Da Vinci robot-assisted radical gastrectomy and open radical gastrectomy.

The purpose of paper is to investigate the depression and anxiety as well as independent influential...

Discriminating Heterogeneous Trajectories of Resilience and Depression After Major Life Stressors Using Polygenic Scores.

IMPORTANCE: Major life stressors, such as loss and trauma, increase the risk of depression. It is kn...

Semi-supervised random forest regression model based on co-training and grouping with information entropy for evaluation of depression symptoms severity.

Semi-supervised learning has always been a hot topic in machine learning. It uses a large number of ...

Identification of Suicide Attempt Risk Factors in a National US Survey Using Machine Learning.

IMPORTANCE: Because more than one-third of people making nonfatal suicide attempts do not receive me...

Machine Learning to Differentiate Risk of Suicide Attempt and Self-harm After General Medical Hospitalization of Women With Mental Illness.

BACKGROUND: Suicide prevention is a public health priority, but risk factors for suicide after medic...

Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression.

IMPORTANCE: Diverse models have been developed to predict psychosis in patients with clinical high-r...

Biologically plausible models of neural dynamics for rapid-acting antidepressant interventions.

[Study on classification and identification of depressed patients and healthy people among adolescents based on optimization of brain characteristics of network].

To enhance the accuracy of computer-aided diagnosis of adolescent depression based on electroencepha...

Machine Learning Analysis of Blood microRNA Data in Major Depression: A Case-Control Study for Biomarker Discovery.

BACKGROUND: There is a lack of reliable biomarkers for major depressive disorder (MDD) in clinical p...

[Effects of acupuncture on Nod-like receptor protein 3 inflammasome signal pathway in the prefrontal cortex of rat with depression].

OBJECTIVE: To observe the influence of acupuncture on the expression of pivotal molecules of Nod-lik...

Depression Severity Assessment for Adolescents at High Risk of Mental Disorders.

Recent progress in artificial intelligence has led to the development of automatic behavioral marker...

AI enabled suicide prediction tools: a qualitative narrative review.

Suicide poses a significant health burden worldwide. In many cases, people at risk of suicide do no...

Predicting Individual Remission After Electroconvulsive Therapy Based on Structural Magnetic Resonance Imaging: A Machine Learning Approach.

OBJECTIVE: To identify important clinical or imaging features predictive of an individual's response...

Anxiety and Depression Diagnosis Method Based on Brain Networks and Convolutional Neural Networks.

At present, only professional doctors can use the professional scales to diagnose depression and anx...