Psychiatry

Depression

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

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Towards a fully automated surveillance of well-being status in laboratory mice using deep learning: Starting with facial expression analysis.

Assessing the well-being of an animal is hindered by the limitations of efficient communication betw...

A social robot intervention on depression, loneliness, and quality of life for Taiwanese older adults in long-term care.

OBJECTIVES: To investigate the effect of a social robot intervention on depression, loneliness, and ...

Recognizing states of psychological vulnerability to suicidal behavior: a Bayesian network of artificial intelligence applied to a clinical sample.

BACKGROUND: This study aimed to determine conditional dependence relationships of variables that con...

Using machine learning to classify suicide attempt history among youth in medical care settings.

BACKGROUND: The current study aimed to classify recent and lifetime suicide attempt history among yo...

Identifying the presence and timing of discrete mood states prior to therapy.

The present study tested a novel, person-specific method for identifying discrete mood profiles from...

Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records.

Suicide is the tenth leading cause of death in the United States (US). An early-warning system (EWS)...

Natural language processing of clinical mental health notes may add predictive value to existing suicide risk models.

BACKGROUND: This study evaluated whether natural language processing (NLP) of psychotherapy note tex...

Predicting individual clinical trajectories of depression with generative embedding.

Patients with major depressive disorder (MDD) show heterogeneous treatment response and highly varia...

Assessing the Heterogeneity of Complaints Related to Tinnitus and Hyperacusis from an Unsupervised Machine Learning Approach: An Exploratory Study.

INTRODUCTION: Subjective tinnitus (ST) and hyperacusis (HA) are common auditory symptoms that may be...

DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning.

Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases....

Predicting future onset of depression among community dwelling adults in the Republic of Korea using a machine learning algorithm.

Because depression has high prevalence and cause enduring disability, it is important to predict ons...

Depression phenotype identified by using single nucleotide exact amplicon sequence variants of the human gut microbiome.

Single nucleotide exact amplicon sequence variants (ASV) of the human gut microbiome were used to ev...

Identifying epilepsy psychiatric comorbidities with machine learning.

OBJECTIVE: People with epilepsy are at increased risk for mental health comorbidities. Machine-learn...

Deep learning-based automated speech detection as a marker of social functioning in late-life depression.

BACKGROUND: Late-life depression (LLD) is associated with poor social functioning. However, previous...

Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression.

IMPORTANCE: Social and economic costs of depression are exacerbated by prolonged periods spent ident...

Seeking for potential pathogenic genes of major depressive disorder in the Gene Expression Omnibus database.

INTRODUCTION: Major depressive disorder (MDD) is one of the most common mental disorders worldwide. ...

Ensemble Learning for Early-Response Prediction of Antidepressant Treatment in Major Depressive Disorder.

BACKGROUND: In order to reduce unsuccessful treatment trials for depression, neuroimaging and geneti...

On-site testing of multiple drugs of abuse in urine by a miniature dual-LIT mass spectrometer.

There is an increasing need for rapid and on-site detection of emerging drugs of abuse. In this work...

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