Depression

Rapid detection of internalizing diagnosis in young children enabled by wearable sensors and machine learning.

There is a critical need for fast, inexpensive, objective, and accurate screening tools for childhood psychopathology. Perhaps most compelling is in the case of internalizing disorders, like anxiety and depression, where unobservable symptoms cause children to go unassessed-suffering in silence because they never exhibiting the disruptive behaviors that would lead to a referral for diagnostic asse...

Leveraging Machine Learning Approaches for Predicting Antidepressant Treatment Response Using Electroencephalography (EEG) and Clinical Data.

Individuals with major depressive disorder (MDD) vary in their response to antidepressants. However, identifying objective biomarkers, prior to or early in the course of treatment that can predict antidepressant efficacy, remains a challenge. Individuals with MDD participated in a 12-week antidepressant pharmacotherapy trial. Electroencephalographic (EEG) data was collected before and 1 week pos...

Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide.

Suicide accounts for nearly 800,000 deaths per year worldwide with rates of both deaths and attempts rising. Family studies have estimated substantial...

Machine learning in suicide science: Applications and ethics.

For decades, our ability to predict suicide has remained at near-chance levels. Machine learning has recently emerged as a promising tool for advancin...

BP neural network prediction model for suicide attempt among Chinese rural residents.

OBJECTIVE: This study aimed to establish and assess the Back Propagation Neural Network (BPNN) prediction model for suicide attempt, so as to improve ...

Predicting persistent depressive symptoms in older adults: A machine learning approach to personalised mental healthcare.

BACKGROUND: Depression causes significant physical and psychosocial morbidity. Predicting persistence of depressive symptoms could permit targeted pre...

Use of natural language processing in electronic medical records to identify pregnant women with suicidal behavior: towards a solution to the complex classification problem.

We developed algorithms to identify pregnant women with suicidal behavior using information extracted from clinical notes by natural language processi...

Ascertaining Depression Severity by Extracting Patient Health Questionnaire-9 (PHQ-9) Scores from Clinical Notes.

The Patient Health Questionnaire-9 (PHQ-9) is a validated instrument for assessing depression severity. While some electronic health record (EHR) syst...

Toward Automatic Risk Assessment to Support Suicide Prevention.

Suicide has been considered an important public health issue for years and is one of the main causes of death worldwide. Despite prevention strategie...

Supervised machine learning to decipher the complex associations between neuro-immune biomarkers and quality of life in schizophrenia.

Stable phase schizophrenia is characterized by altered patterning in tryptophan catabolites (TRYCATs) and memory impairments, which are associated wit...

The use of machine learning in the study of suicidal and non-suicidal self-injurious thoughts and behaviors: A systematic review.

BACKGROUND: Machine learning techniques offer promise to improve suicide risk prediction. In the current systematic review, we aimed to review the exi...

A machine learning ensemble to predict treatment outcomes following an Internet intervention for depression.

BACKGROUND: Some Internet interventions are regarded as effective treatments for adult depression, but less is known about who responds to this form o...

Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approach.

Many variables have been linked to different course trajectories of depression. These findings, however, are based on group comparisons with unknown t...

Psychiatric stressor recognition from clinical notes to reveal association with suicide.

Suicide takes the lives of nearly a million people each year and it is a tremendous economic burden globally. One important type of suicide risk facto...

Predicting Adherence to Internet-Delivered Psychotherapy for Symptoms of Depression and Anxiety After Myocardial Infarction: Machine Learning Insights From the U-CARE Heart Randomized Controlled Trial.

BACKGROUND: Low adherence to recommended treatments is a multifactorial problem for patients in rehabilitation after myocardial infarction (MI). In a ...

Primal world beliefs.

Beck's insight-that beliefs about one's self, future, and environment shape behavior-transformed depression treatment. Yet environment beliefs remain ...

Fusing Location Data for Depression Prediction.

Recent studies have demonstrated that geographic location features collected using smartphones can be a powerful predictor for depression. While locat...

Social Robots for Depression in Older Adults: A Systematic Review.

PURPOSE: In recent years, there has been an increase in the number of studies using social robots to improve psychological well-being. This systematic...

Machine learning in major depression: From classification to treatment outcome prediction.

AIMS: Major depression disorder (MDD) is the single greatest cause of disability and morbidity, and affects about 10% of the population worldwide. Cur...

Prevalence of and factors related to anxiety and depression symptoms among married patients with gynecological malignancies in China.

OBJECTIVE: This study aims to investigate the prevalence of anxiety and depression among married patients with gynecological malignancies in China and...