Psychiatry

Stress Monitoring in Pandemic Screening: Insights from GSR Sensor and Machine Learning Analysis.

This study investigates the impact of patient stress on COVID-19 screening. An attempt was made to measure the level of anxiety of individuals undertaking rapid tests for SARS-CoV-2. To this end, a galvanic skin response (GSR) sensor that was connected to a microcontroller was used to record the individual stress levels. GSR data were collected from 51 individuals at SARS-CoV-2 testing sites. The ...

Predicting working alliance in psychotherapy: A multi-modal machine learning approach.

OBJECTIVE: Session-by-session tracking of the working alliance enables clinicians to detect alliance deterioration and intervene accordingly, which has shown to improve treatment outcome, and reduce dropout. Despite this, regular use of alliance self-report measures has failed to gain widespread implementation. We aimed to develop an automated alliance prediction using behavioral features obtained...

Deep learning predicts DNA methylation regulatory variants in specific brain cell types and enhances fine mapping for brain disorders.

DNA methylation (DNAm) is essential for brain development and function and potentially mediates the effects of genetic risk variants underlying brain ...

Analysis of User-Generated Posts on Social Media of Adjuvant Analgesics: A Machine Learning Study.

Antiepileptics and antidepressants are frequently prescribed for chronic pain, but their efficacy and potential adverse effects raise concerns, inclu...

Virtual reality-assisted prediction of adult ADHD based on eye tracking, EEG, actigraphy and behavioral indices: a machine learning analysis of independent training and test samples.

Given the heterogeneous nature of attention-deficit/hyperactivity disorder (ADHD) and the absence of established biomarkers, accurate diagnosis and ef...

Machine learning and confirmatory factor analysis show that buprenorphine alters motor and anxiety-like behaviors in male, female, and obese C57BL/6J mice.

Buprenorphine is an opioid approved for medication-assisted treatment of opioid use disorder. Used off-label, buprenorphine has been reported to contr...

On the improvement of schizophrenia detection with optical coherence tomography data using deep neural networks and aggregation functions.

Schizophrenia is a serious mental disorder with a complex neurobiological background and a well-defined psychopathological picture. Despite many effor...

Utilising AI technique to identify depression risk among doctoral students.

The phenomenon that the depression risk among doctoral students is higher than that of other groups should not be ignored. Despite this, studies speci...

Evaluating of BERT-based and Large Language Mod for Suicide Detection, Prevention, and Risk Assessment: A Systematic Review.

Suicide constitutes a public health issue of major concern. Ongoing progress in the field of artificial intelligence, particularly in the domain of la...

Diagnosis of Autism Spectrum Disorder (ASD) by Dynamic Functional Connectivity Using GNN-LSTM.

Early detection of autism spectrum disorder (ASD) is particularly important given its insidious qualities and the high cost of the diagnostic process....

Estimating cardiovascular mortality in patients with hypertension using machine learning: The role of depression classification based on lifestyle and physical activity.

PURPOSE: This study aims to harness machine learning techniques, particularly the Random Survival Forest (RSF) model, to assess the impact of depressi...

Machine learning prediction model of the treatment response in schizophrenia reveals the importance of metabolic and subjective characteristics.

Predicting early treatment response in schizophrenia is pivotal for selecting the best therapeutic approach. Utilizing machine learning (ML) technique...

Diagnosis of major depressive disorder using a novel interpretable GCN model based on resting state fMRI.

The diagnosis and analysis of major depressive disorder (MDD) faces some intractable challenges such as dataset limitations and clinical variability. ...

Development of a short form of the Geriatric Depression Scale-30 based on item response theory and the RiskSLIM algorithm.

Recently, methods of quickly and accurately screening for geriatric depression have attracted substantial attention. Short forms of the 30-item Geriat...

Multi-modal cross-domain self-supervised pre-training for fMRI and EEG fusion.

Neuroimaging techniques including functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) have shown promise in detecting function...

Objective approach to diagnosing attention deficit hyperactivity disorder by using pixel subtraction and machine learning classification of outpatient consultation videos.

BACKGROUND: Attention deficit hyperactivity disorder (ADHD) is a common childhood neurodevelopmental disorder, affecting between 5% and 7% of school-a...

MCBERT: A multi-modal framework for the diagnosis of autism spectrum disorder.

Within the domain of neurodevelopmental disorders, autism spectrum disorder (ASD) emerges as a distinctive neurological condition characterized by mul...

Momentary Depression Severity Prediction in Patients With Acute Depression Who Undergo Sleep Deprivation Therapy: Speech-Based Machine Learning Approach.

BACKGROUND: Mobile devices for remote monitoring are inevitable tools to support treatment and patient care, especially in recurrent diseases such as ...

Exploring correlates of high psychiatric inpatient utilization in Switzerland: a descriptive and machine learning analysis.

BACKGROUND: This study investigated socio-demographic, psychiatric, and psychological characteristics of patients with high versus low utilization of ...

Machine learning analysis of factors affecting college students' academic performance.

This study aims to explore various key factors influencing the academic performance of college students, including metacognitive awareness, learning m...