Depression

See your mental state from your walk: Recognizing anxiety and depression through Kinect-recorded gait data.

As the challenge of mental health problems such as anxiety and depression increasing today, more convenient, objective, real-time assessing techniques of mental state are in need. The Microsoft Kinect camera is a possible option for contactlessly capturing human gait, which could reflect the walkers' mental state. So we tried to propose a novel method for monitoring individual's anxiety and depres...

Proactive Suicide Prevention Online (PSPO): Machine Identification and Crisis Management for Chinese Social Media Users With Suicidal Thoughts and Behaviors.

BACKGROUND: Suicide is a great public health challenge. Two hundred million people attempt suicide in China annually. Existing suicide prevention programs require the help-seeking initiative of suicidal individuals, but many of them have a low motivation to seek the required help. We propose that a proactive and targeted suicide prevention strategy can prompt more people with suicidal thoughts and...

Detecting depression using a framework combining deep multimodal neural networks with a purpose-built automated evaluation.

Machine learning (ML) has been introduced into the medical field as a means to provide diagnostic tools capable of enhancing accuracy and precision wh...

Giving Voice to Vulnerable Children: Machine Learning Analysis of Speech Detects Anxiety and Depression in Early Childhood.

Childhood anxiety and depression often go undiagnosed. If left untreated these conditions, collectively known as internalizing disorders, are associat...

Spiking Neural Network Modelling Approach Reveals How Mindfulness Training Rewires the Brain.

There has been substantial interest in Mindfulness Training (MT) to understand how it can benefit healthy individuals as well as people with a broad r...

Use of machine learning in predicting clinical response to transcranial magnetic stimulation in comorbid posttraumatic stress disorder and major depression: A resting state electroencephalography study.

BACKGROUND: Repetitive transcranial magnetic stimulation (TMS) is clinically effective for major depressive disorder (MDD) and investigational for oth...

Relative importance of symptoms, cognition, and other multilevel variables for psychiatric disease classifications by machine learning.

This study used machine-learning algorithms to make unbiased estimates of the relative importance of various multilevel data for classifying cases wit...

Evaluating the evidence for biotypes of depression: Methodological replication and extension of.

BACKGROUND: Psychiatric disorders are highly heterogeneous, defined based on symptoms with little connection to potential underlying biological mechan...

Classifying major depression patients and healthy controls using EEG, eye tracking and galvanic skin response data.

OBJECTIVE: Major depression disorder (MDD) is one of the most prevalent mental disorders worldwide. Diagnosing depression in the early stage is crucia...

Artificial intelligence based discovery of the association between depression and chronic fatigue syndrome.

BACKGROUND: Both of the modern medicine and the traditional Chinese medicine classify depressive disorder (DD) and chronic fatigue syndrome (CFS) to o...

EEG-based mild depression recognition using convolutional neural network.

Electroencephalography (EEG)-based studies focus on depression recognition using data mining methods, while those on mild depression are yet in infanc...

Identification of suicidal behavior among psychiatrically hospitalized adolescents using natural language processing and machine learning of electronic health records.

OBJECTIVE: The rapid proliferation of machine learning research using electronic health records to classify healthcare outcomes offers an opportunity ...

Low-rank network signatures in the triple network separate schizophrenia and major depressive disorder.

Brain imaging studies have revealed that functional and structural brain connectivity in the so-called triple network (i.e., default mode network (DMN...

A randomized controlled trial of suicide prevention training for primary care providers: a study protocol.

BACKGROUND: Suicide is a national public health crisis and a critical patient safety issue. It is the 10th leading cause of death overall and the seco...

Electroconvulsive Therapy Induces Cortical Morphological Alterations in Major Depressive Disorder Revealed with Surface-Based Morphometry Analysis.

Although electroconvulsive therapy (ECT) is one of the most effective treatments for major depressive disorder (MDD), the mechanism underlying the the...

Predicting inadequate postoperative pain management in depressed patients: A machine learning approach.

Widely-prescribed prodrug opioids (e.g., hydrocodone) require conversion by liver enzyme CYP-2D6 to exert their analgesic effects. The most commonly p...

Outcome-Weighted Learning for Personalized Medicine with Multiple Treatment Options.

To achieve personalized medicine, an individualized treatment strategy assigning treatment based on an individual's characteristics that leads to the ...

Research Domain Criteria scores estimated through natural language processing are associated with risk for suicide and accidental death.

BACKGROUND: Identification of individuals at increased risk for suicide is an important public health priority, but the extent to which considering cl...

Quantitative Electroencephalography in Guiding Treatment of Major Depression.

This paper reviews significant contributions to the evidence for the use of quantitative electroencephalography features as biomarkers of depression t...

Recent Developments in the Treatment of Depression.

The cognitive and behavioral interventions can be as efficacious as antidepressant medications and more enduring, but some patients will be more likel...