Depression

Developing an interpretable machine learning model for screening depression in older adults with functional disability.

This study utilized data from the 2020 wave of the China Health and Retirement Longitudinal Study da...

Machine learning models for diagnosis and risk prediction in eating disorders, depression, and alcohol use disorder.

BACKGROUND: Early diagnosis and treatment of mental illnesses is hampered by the lack of reliable ma...

BERT and BERTopic for screening clinical depression on open-ended text messages collected through a mobile application from older adults.

BACKGROUND: Despite the high suicide rate in South Korea, older adults are reluctant to see a psychi...

Predicting Stress, Anxiety, and Depression in Adult Men Based on Nutritional and Lifestyle Variables: A Comparative Analysis of Machine Learning Methods.

Mental health disorders like depression, anxiety, and stress (DAS) are rising globally. Understandin...

An approach to screen susceptible rats and efficacy of an antidepressive treatment after chronic stress.

Chronic stress during life has been considered a risk factor for the development of psychiatric illn...

Links between self-monitoring data collected through smartphones and smartwatches and the individual disease trajectories of adult patients with depressive disorders: Study protocol of a one-year observational trial.

Depression is highly recurrent and heterogenous in its individual course, requiring a personalized t...

HEDL: Deep learning multiple approaches for early detection of depression using sarcastic text.

Sarcasm is particularly notorious towards mental health, and thus it is quite essential for early id...

Can Electroencephalography and Machine Learning Predict Treatment Response to Transcranial Magnetic Stimulation in Depression?

Comprehensive Characterization of Antidepressant Pharmacogenetics: A Systematic Review of Studies in Major Depressive Disorder.

Pharmacogenetics is a promising strategy to facilitate individualized care for patients with Major D...

Differentiating Treatment-Resistant Depression With and Without Parkinsonism in the Elderly From a Psychiatric Perspective by Tc-TRODAT-1 SPECT Imaging.

OBJECTIVES: Late-life depression often overlaps with neurodegenerative diseases leading to diagnosti...

Stratifying Risk for Postpartum Depression at Time of Hospital Discharge.

OBJECTIVE: Postpartum depression (PPD) is a major contributor to postpartum morbidity and mortality....

Suicide ideation detection based on documents dimensionality expansion.

Accurate and secure classifying informal documents related to mental disorders is challenging due to...

Postoperative self-care ability of continuous nursing based on artificial intelligence for stroke patients with neurological injury.

According to the statistics of relevant data, stroke is a relatively common cerebrovascular disease,...

Effect of Cumulative Exposure on the Efficacy of Paroxetine: A Population Pharmacokinetic-Pharmacodynamic and Machine Learning Analyses.

Selective serotonin reuptake inhibitors (SSRIs) are widely used in depression treatment. However, th...

Question-based computational language approach outperform ratings scale in discriminating between anxiety and depression.

Major Depression (MD) and General Anxiety Disorder (GAD) are the most common mental health disorders...

Advances in EEG-based detection of Major Depressive Disorder using shallow and deep learning techniques: A systematic review.

The contemporary diagnosis of Major Depressive Disorder (MDD) primarily relies on subjective assessm...

Assessing bias in AI-driven psychiatric recommendations: A comparative cross-sectional study of chatbot-classified and CANMAT 2023 guideline for adjunctive therapy in difficult-to-treat depression.

The integration of chatbots into psychiatry introduces a novel approach to support clinical decision...

Combining Machine Learning and Comparative Effectiveness Methodology to Study Primary Care Pharmacotherapy Pathways for Veterans With Depression.

OBJECTIVES: To demonstrate an innovative method combining machine learning with comparative effectiv...

Predictors of smartphone addiction in adolescents with depression: combing the machine learning and moderated mediation model approach.

Smartphone addiction (SA) significantly impacts the physical and mental health of adolescents, and c...

A novel artificial intelligence-based methodology to predict non-specific response to treatment.

Non-specific response to treatment (NSRT) is the primary contributor to the failure of randomized cl...

Mental disorder preventing by worry levels detection in social media using deep learning based on psycho-linguistic features: application on the COVID-19 lockdown period.

BACKGROUND: The COVID-19 pandemic has had a profound effect on the daily routines of individuals and...