Psychiatry

Protocol for Designing a Model to Predict the Likelihood of Psychosis From Electronic Health Records Using Natural Language Processing and Machine Learning.

INTRODUCTION: Rapid identification of individuals developing a psychotic spectrum disorder (PSD) is ...

Using machine learning models to identify the risk of depression in middle-aged and older adults with frequent and infrequent nicotine use: A cross-sectional study.

BACKGROUND: Depression is very prevalent in middle-aged and older smokers. Therefore, we aimed to id...

Fractional whale driving training-based optimization enabled transfer learning for detecting autism spectrum disorder.

Autism Spectrum Disorder (ASD) is a neurological illness that degrades communication and interaction...

Forecasting the incidence frequencies of schizophrenia using deep learning.

Mental disorders are becoming increasingly prevalent worldwide, and accurate incidence forecasting i...

Machine Learning Model Reveals Determinators for Admission to Acute Mental Health Wards From Emergency Department Presentations.

This research addresses the critical issue of identifying factors contributing to admissions to acut...

DepressionEmo: A novel dataset for multilabel classification of depression emotions.

Emotions are integral to human social interactions, with diverse responses elicited by various situa...

Schizophrenia diagnosis using the GRU-layer's alpha-EEG rhythm's dependability.

Verifying schizophrenia (SZ) can be assisted by deep learning techniques and patterns in brain activ...

A neural network approach to predict opioid misuse among previously hospitalized patients using electronic health records.

Can Electronic Health Records (EHR) predict opioid misuse in general patient populations? This resea...

An artificial intelligence tool to assess the risk of severe mental distress among college students in terms of demographics, eating habits, lifestyles, and sport habits: an externally validated study using machine learning.

BACKGROUND: Precisely estimating the probability of mental health challenges among college students ...

Mental issues, internet addiction and quality of life predict burnout among Hungarian teachers: a machine learning analysis.

BACKGROUND: Burnout is usually defined as a state of emotional, physical, and mental exhaustion that...

Characterizing Autism Spectrum Disorder Through Fusion of Local Cortical Activation and Global Functional Connectivity Using Game-Based Stimuli and a Mobile EEG System.

The deficit in social interaction skills among individuals with autism spectrum disorder (ASD) is st...

Identification of Pyroptosis-Related Molecular Subtypes and Diagnostic Model development in Major Depressive Disorder.

Major depressive disorder (MDD) is a prevalent psychological disorder associated with inflammation, ...

Hybrid similarity based feature selection and cascade deep maxout fuzzy network for Autism Spectrum Disorder detection using EEG signal.

Autism Spectrum Disorder (ASD) is a neurological disorder that influences a person's comprehension a...

Effect of a Group-Based Personal Assistive RObot (PARO) Robot Intervention on Cognitive Function, Autonomic Nervous System Function, and Mental Well-being in Older Adults with Mild Dementia: A Randomized Controlled Trial.

OBJECTIVES: Personal Assistive RObot (PARO) interventions have been used to treat agitation and mood...

Determinants of adoption of household water treatment in Haiti using two analysis methods: logistic regression and machine learning.

Household water treatment (HWT) is recommended when safe drinking water is limited. To understand de...

Self-Administered Interventions Based on Natural Language Processing Models for Reducing Depressive and Anxiety Symptoms: Systematic Review and Meta-Analysis.

BACKGROUND: The introduction of natural language processing (NLP) technologies has significantly enh...

Machine Learning, Deep Learning, and Data Preprocessing Techniques for Detecting, Predicting, and Monitoring Stress and Stress-Related Mental Disorders: Scoping Review.

BACKGROUND: Mental stress and its consequent mental health disorders (MDs) constitute a significant ...

Designing AI for mental health diagnosis: responding to critics.

Old wine in new bottles? What is new with AI for mental health diagnosis?

Machine learning for mental health diagnosis: tackling contributory injustice and epistemic oppression.

Navigating cultural diversity: harnessing AI for mental health diagnosis despite value-laden judgements.