Psychiatry

Machine-Learning Mental-Fatigue-Measuring μm-Thick Elastic Epidermal Electronics (MMMEEE).

Electrophysiological (EP) signals are key biomarkers for monitoring mental fatigue (MF) and general health, but state-of-the-art wearable EP-based MF monitoring systems are bulky and require user-specific, labeled data. Ultrathin epidermal electrodes with high performance are ideal for constructing imperceptive EP sensing systems; however, the lack of a simple and scalable fabrication delays their...

The efficacy of topological properties of functional brain networks in identifying major depressive disorder.

Major Depressive Disorder (MDD) is a common mental disorder characterized by cognitive impairment, and its pathophysiology remains to be explored. In this study, we aimed to explore the efficacy of brain network topological properties (TPs) in identifying MDD patients, revealing variational brain regions with efficient TPs. Functional connectivity (FC) networks were constructed from resting-state ...

Current update on the neurological manifestations of long COVID: more questions than answers.

Since the outbreak of the COVID-19 pandemic, there has been a global surge in patients presenting with prolonged or late-onset debilitating sequelae o...

BPEN: Brain Posterior Evidential Network for trustworthy brain imaging analysis.

The application of deep learning techniques to analyze brain functional magnetic resonance imaging (fMRI) data has led to significant advancements in ...

Disentangling the Genetic Landscape of Peripartum Depression: A Multi-Polygenic Machine Learning Approach on an Italian Sample.

BACKGROUND: The genetic determinants of peripartum depression (PPD) are not fully understood. Using a multi-polygenic score approach, we characterized...

Altered Blood Oxygen Level-Dependent Signal Stability in the Brain of Patients with Major Depressive Disorder Undergoing Resting-State Functional Magnetic Resonance Imaging.

INTRODUCTION: Major depressive disorder (MDD) is a common, relapse-prone psychiatric disorder with unknown pathogenesis. Previous studies on resting-s...

The relationship between artificial intelligence anxiety and unemployment anxiety among university students.

BackgroundThe idea that people will lose their jobs because of robots with artificial intelligence is one of the biggest recent concerns about artific...

Multimodal machine learning for language and speech markers identification in mental health.

BACKGROUND: There are numerous papers focusing on diagnosing mental health disorders using unimodal and multimodal approaches. However, our literature...

Evolution of Linguistic Markers of Agency, Centrality and Content During Metacognitive Therapy for Psychosis: A Pilot Exploratory Study.

AIM: Metacognitive Reflection and Insight Therapy (MERIT) is a form of person-centred psychotherapy that promotes recovery-oriented outcomes by target...

Exploring sensory alterations and repetitive behaviors in children with autism spectrum disorder from the perspective of artificial neural networks.

BACKGROUND: Restrictive repetitive behaviors (RRBs) and sensory processing disorders are core symptoms of autism spectrum disorder (ASD). Their relati...

Early diagnostic value of home video-based machine learning in autism spectrum disorder: a meta-analysis.

UNLABELLED: Machine learning (ML) based on remote video has shown ideal diagnostic value in autism spectrum disorder (ASD). Here, we conducted a meta-...

A digital phenotyping dataset for impending panic symptoms: a prospective longitudinal study.

This study investigated the utilization of digital phenotypes and machine learning algorithms to predict impending panic symptoms in patients with moo...

Predicting suicidal behavior outcomes: an analysis of key factors and machine learning models.

BACKGROUND: Suicidal behaviors, which may lead to death (suicide) or survival (suicide attempt), are influenced by various factors. Identifying the sp...

Assessment of non-fatal injuries among university students in Hainan: a machine learning approach to exploring key factors.

BACKGROUND: Injuries constitute a significant global public health concern, particularly among individuals aged 0-34. These injuries are affected by v...

Wearable EEG Neurofeedback Based-on Machine Learning Algorithms for Children with Autism: A Randomized, Placebo-controlled Study.

OBJECTIVE: Behavioral interventions have been shown to ameliorate the electroencephalogram (EEG) dynamics underlying the behavioral symptoms of autism...

The help-seeking process and predictors of mental health care use among individuals with depressive symptoms: a machine learning approach.

PURPOSE: The goal of the study was to identify the most important influences on professional healthcare use of people with depressive symptoms. We inc...

Evaluating predictive artificial intelligence approaches used in mobile health platforms to forecast mental health symptoms among youth: a systematic review.

The youth mental health crisis is exacerbated by limited access to care and resources. Mobile health (mHealth) platforms using predictive artificial i...

Development of machine learning models for predicting depressive symptoms in knee osteoarthritis patients.

Knee osteoarthritis (KOA) combined with depressive symptoms is prevalent and leads to poor outcomes and significant financial burdens. However, practi...

Development and Validation of a Prediction Model for Co-Occurring Moderate-to-Severe Anxiety Symptoms in First-Episode and Drug Naïve Patients With Major Depressive Disorder.

Moderate-to-severe anxiety symptoms are severe and common in patients with major depressive disorder (MDD) and have a significant impact on MDD patie...

The application of artificial intelligence in health communication development: A scoping review.

This scoping review explores the integration of Artificial Intelligence (AI) with communication, behavioral, and social theories to enhance health beh...