Psychiatry

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

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Emotional hyper-reactivity and cardiometabolic risk in remitted bipolar patients: a machine learning approach.

OBJECTIVE: Remitted bipolar disorder (BD) patients frequently present with chronic mood instability ...

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 ...

Using machine learning to identify patterns of lifetime health problems in decedents with autism spectrum disorder.

Very little is known about the health problems experienced by individuals with autism spectrum disor...

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 ...

Risk Assessment for Parents Who Suspect Their Child Has Autism Spectrum Disorder: Machine Learning Approach.

BACKGROUND: Parents are likely to seek Web-based communities to verify their suspicions of autism sp...

Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning.

Psychiatric prognosis is a difficult problem. Making a prognosis requires looking far into the futur...

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...

Plasma Neuropeptide-S Levels in Populations Diagnosed with Generalized Anxiety Disorder: A Controlled Study.

INTRODUCTION: Neuropeptide S (NPS) is a novel neuropeptide reported to be involved in fear-and stres...

Automated EEG-based screening of depression using deep convolutional neural network.

In recent years, advanced neurocomputing and machine learning techniques have been used for Electroe...

Predicting lithium treatment response in bipolar patients using gender-specific gene expression biomarkers and machine learning.

We sought to test the hypothesis that transcriptome-level gene signatures are differentially expres...

Leveraging existing corpora for de-identification of psychiatric notes using domain adaptation.

De-identification of clinical notes is a special case of named entity recognition. Supervised machin...

Machine learning classification of first-episode schizophrenia spectrum disorders and controls using whole brain white matter fractional anisotropy.

BACKGROUND: Early diagnosis of schizophrenia could improve the outcome of the illness. Unlike classi...

Psychosocial factors associated with intended use of automated vehicles: A simulated driving study.

This study applied the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) to...

Predicting individual responses to the electroconvulsive therapy with hippocampal subfield volumes in major depression disorder.

Electroconvulsive therapy (ECT) is one of the most effective treatments for major depression disorde...

Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI.

BACKGROUND: A lack of a sufficiently large sample at single sites causes poor generalizability in au...

Determining Anxiety in Obsessive Compulsive Disorder through Behavioural Clustering and Variations in Repetition Intensity.

BACKGROUND AND OBJECTIVES: Over the last decade, the application of computer vision techniques to th...

Reflecting on the Germanwings Disaster: A Systematic Review of Depression and Suicide in Commercial Airline Pilots.

BACKGROUND: The 2015 Germanwings Flight 9525 disaster, in which 150 people were killed after the co-...

Thalamocortical dysrhythmia detected by machine learning.

Thalamocortical dysrhythmia (TCD) is a model proposed to explain divergent neurological disorders. I...

Investigating brain structural patterns in first episode psychosis and schizophrenia using MRI and a machine learning approach.

In this study, we employed the Maximum Uncertainty Linear Discriminant Analysis (MLDA) to investigat...

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