Depression

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 medical hospitalization remain understudied. This problem is critical for women, for whom suicide rates in the United States are disproportionately increasing.

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 electroencephalogram signals, this study collected signals of 32...

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 practice. However, several studies have shown an as...

[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-like receptor protein 3(NLRP3) inflammasome signal pa...

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 not engage with their doctor or community due to con...

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 recognition, such as facial and vocal expressions...

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 to electroconvulsive therapy (ECT) by utilizing a...

Can machine-learning methods really help predict suicide?

PURPOSE OF REVIEW: In recent years there has been interest in the use of machine learning in suicide research in reaction to the failure of traditiona...

Depression Scale Prediction with Cross-Sample Entropy and Deep Learning.

A two-stage deep learning-based scheme is presented to predict the Hamilton Depression Scale (HAM-D) in this study. First, the cross-sample entropy (C...

EEG-based Depression Detection Using Convolutional Neural Network with Demographic Attention Mechanism.

Electroencephalography (EEG)-based depression detection has become a hot topic in the development of biomedical engineering. However, the complexity a...

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 anxiety in clinical practice. In recent years, the pr...

Antidepressant pathways of the Chinese herb through genetic ontology analysis.

Active compounds and corresponding targets of the traditional Chinese herb, were obtained from systems pharmacological database and placed into ClueG...

Depression screening using mobile phone usage metadata: a machine learning approach.

OBJECTIVE: Depression is currently the second most significant contributor to non-fatal disease burdens globally. While it is treatable, depression re...

Prediction of Sex-Specific Suicide Risk Using Machine Learning and Single-Payer Health Care Registry Data From Denmark.

IMPORTANCE: Suicide is a public health problem, with multiple causes that are poorly understood. The increased focus on combining health care data wit...

Targeted prescription of cognitive-behavioral therapy versus person-centered counseling for depression using a machine learning approach.

OBJECTIVE: Depression is a highly common mental disorder and a major cause of disability worldwide. Several psychological interventions are available,...

Personalized prognostic prediction of treatment outcome for depressed patients in a naturalistic psychiatric hospital setting: A comparison of machine learning approaches.

OBJECTIVE: Research on predictors of treatment outcome in depression has largely derived from randomized clinical trials involving strict standardizat...

Changes in Functional Connectivity Predict Outcome of Repetitive Transcranial Magnetic Stimulation Treatment of Major Depressive Disorder.

Repetitive transcranial magnetic stimulation (rTMS) treatment of major depressive disorder (MDD) is associated with changes in brain functional connec...

What health records data are required for accurate prediction of suicidal behavior?

OBJECTIVE: The study sought to evaluate how availability of different types of health records data affect the accuracy of machine learning models pred...

Applying Natural Language Processing to Evaluate News Media Coverage of Bullying and Cyberbullying.

Bullying events have frequently been the focus of coverage by news media, including news stories about teens whose death from suicide was attributed t...