Psychiatry

Depression

Latest AI and machine learning research in depression for healthcare professionals.

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Fusing Location Data for Depression Prediction.

Recent studies have demonstrated that geographic location features collected using smartphones can b...

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...

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 ...

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 pat...

Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review.

BACKGROUND: No previous study has comprehensively reviewed the application of machine learning algor...

Predeployment predictors of psychiatric disorder-symptoms and interpersonal violence during combat deployment.

BACKGROUND: Preventing suicides, mental disorders, and noncombat-related interpersonal violence duri...

Exploring the prediction of emotional valence and pharmacologic effect across fMRI studies of antidepressants.

BACKGROUND: Clinically approved antidepressants modulate the brain's emotional valence circuits, sug...

Self-Efficacy, Poststroke Depression, and Rehabilitation Outcomes: Is There a Correlation?

BACKGROUND: The sudden live changes of stroke survivors may lead to negative psychological and behav...

Extracting psychiatric stressors for suicide from social media using deep learning.

BACKGROUND: Suicide has been one of the leading causes of deaths in the United States. One major cau...

Drug Repositioning for Schizophrenia and Depression/Anxiety Disorders: A Machine Learning Approach Leveraging Expression Data.

Development of new medications is a lengthy and costly process, and drug repositioning might help to...

Mining patterns of comorbidity evolution in patients with multiple chronic conditions using unsupervised multi-level temporal Bayesian network.

Over the past few decades, the rise of multiple chronic conditions has become a major concern for cl...

Predictive modeling of treatment resistant depression using data from STAR*D and an independent clinical study.

Identification of risk factors of treatment resistance may be useful to guide treatment selection, a...

Automated depression analysis using convolutional neural networks from speech.

To help clinicians to efficiently diagnose the severity of a person's depression, the affective comp...

Factors associated with dementia in elderly.

We analyzed the factors associated with dementia in the elderly attended at a memory outpatient clin...

Survey of potential receptivity to robotic-assisted exercise coaching in a diverse sample of smokers and nonsmokers.

A prior project found that an intensive (12 weeks, thrice weekly sessions) in-person, supervised, ex...

Identifying Suicide Ideation and Suicidal Attempts in a Psychiatric Clinical Research Database using Natural Language Processing.

Research into suicide prevention has been hampered by methodological limitations such as low sample ...

Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning.

BACKGROUND: Adolescents have high rates of nonfatal suicide attempts, but clinically practical risk ...

A morphometric signature of depressive symptoms in unmedicated patients with mood disorders.

OBJECTIVE: A growing literature indicates that unipolar depression and bipolar depression are associ...

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