Depression

The risks of risk. Regulating the use of machine learning for psychosis prediction.

Recent advances in Machine Learning (ML) have the potential to revolutionise psychosis prediction and psychiatric assessment. This article has two objectives. First, it clarifies which aspects of English Law are relevant in order to regulate the use of ML in clinical research on psychosis prediction. It is argued that its lawful implementation will depend upon the legal requirements regarding the ...

Can Machine Learning help us in dealing with treatment resistant depression? A review.

BACKGROUND: About one third of patients treated with antidepressant do not show sufficient symptoms relief and up to 15% of patients remain symptomatic even after multiple trials are applied, configuring a state called treatment resistant depression (TRD). A clear definition of this state and the understanding of underlying mechanisms contributing to chronic disability caused by major depressive d...

Detection of major depressive disorder from linear and nonlinear heart rate variability features during mental task protocol.

BACKGROUND: Major depressive disorder (MDD) is one of the leading causes of disability; however, current MDD diagnosis methods lack an objective asses...

Comparison of Magnesium Sulfate and Ketamine with Ropivacaine in Supraclavicular Brachial Plexus Block: A Randomized Controlled Trial.

BACKGROUND: Supraclavicular brachial plexus block offers good operating conditions with limited postoperative analgesia. Magnesium sulfate (MgSO) and ...

Predicting death by suicide using administrative health care system data: Can feedforward neural network models improve upon logistic regression models?

BACKGROUND: Suicide is a leading cause of death worldwide. With the increasing volume of administrative health care data, there is an opportunity to e...

The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors.

OBJECTIVE: Suicide is a growing public health concern with a global prevalence of approximately 800,000 deaths per year. The current process of evalua...

Identification of Patients in Need of Advanced Care for Depression Using Data Extracted From a Statewide Health Information Exchange: A Machine Learning Approach.

BACKGROUND: As the most commonly occurring form of mental illness worldwide, depression poses significant health and economic burdens to both the indi...

Depression recognition using machine learning methods with different feature generation strategies.

The diagnosis of depression almost exclusively depends on doctor-patient communication and scale analysis, which have the obvious disadvantages such a...

Identifying depression in the National Health and Nutrition Examination Survey data using a deep learning algorithm.

BACKGROUND: As depression is the leading cause of disability worldwide, large-scale surveys have been conducted to establish the occurrence and risk f...

Application of machine learning to structural connectome to predict symptom reduction in depressed adolescents with cognitive behavioral therapy (CBT).

PURPOSE: Adolescent major depressive disorder (MDD) is a highly prevalent, incapacitating and costly illness. Many depressed teens do not improve with...

Spectral signatures of serotonergic psychedelics and glutamatergic dissociatives.

Classic serotonergic psychedelics are remarkable for their capacity to induce reversible alterations in consciousness of the self and the surroundings...

The Current Research Landscape on the Artificial Intelligence Application in the Management of Depressive Disorders: A Bibliometric Analysis.

Artificial intelligence (AI)-based techniques have been widely applied in depression research and treatment. Nonetheless, there is currently no system...

Machine-learning based brain age estimation in major depression showing no evidence of accelerated aging.

Molecular biological findings indicate that affective disorders are associated with processes akin to accelerated aging of the brain. The use of the B...

Using Machine Learning to Identify Suicide Risk: A Classification Tree Approach to Prospectively Identify Adolescent Suicide Attempters.

This study applies classification tree analysis to prospectively identify suicide attempters among a large adolescent community sample, to demonstrate...

Early Detection of Depression: Social Network Analysis and Random Forest Techniques.

BACKGROUND: Major depressive disorder (MDD) or depression is among the most prevalent psychiatric disorders, affecting more than 300 million people gl...

Using heart rate profiles during sleep as a biomarker of depression.

BACKGROUND: Abnormalities in heart rate during sleep linked to impaired neuro-cardiac modulation may provide new information about physiological sleep...

Prediction models for high risk of suicide in Korean adolescents using machine learning techniques.

OBJECTIVE: Suicide in adolescents is a major problem worldwide and previous history of suicide ideation and attempt represents the strongest predictor...

Prediction of rTMS treatment response in major depressive disorder using machine learning techniques and nonlinear features of EEG signal.

BACKGROUND: Prediction of therapeutic outcome of repetitive transcranial magnetic stimulation (rTMS) treatment is an important purpose that eliminates...

Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A Meta-Analysis.

A psychological disorder is a mutilation state of the body that intervenes the imperative functioning of the mind or brain. In the last few years, the...

Deep Sequential Models for Suicidal Ideation From Multiple Source Data.

This paper presents a novel method for predicting suicidal ideation from electronic health records (EHR) and ecological momentary assessment (EMA) dat...