Psychiatry

Depression screening with textual and audio features based on large language models and machine learning.

BACKGROUND: Depression is a complex disorder that cannot be fully screened by textual features alone, as audio features capture additional psychomotor and affective changes. This study integrates textual and audio features for depression screening and compares the performance of various machine learning models. METHODS: This study used a large-scale, multimodal psychology dataset of 1275 participa...

Emotion classification using gait biomechanics and machine learning.

BACKGROUND: Emerging research suggests that emotions alter the way people walk, suggesting gait data may serve as a potential source for emotion recognition. Compared to traditional modalities (i.e., facial expressions, speech), gait-based emotion detection may have advantages, including reduced susceptibility to deliberate manipulation. RESEARCH QUESTION: Is it feasible to recognize emotional sta...

Mental health at risk: Predicting psychological distress in Australian youth through machine learning models.

BACKGROUND: Psychological distress among youth is a growing global health concern and a leading non-communicable disease burden. Early and accurate pr...

Identifying behavioral and biological predictors of cannabis vapor self-administration in rats.

The recent wave of recreational cannabis legalization in the US has underscored the importance of identifying predictors of individual variability in ...

Suicidal thoughts and behaviours among construction workers: Identifying risks and protective factors.

BACKGROUND: The construction industry records concerningly high rates of suicide compared to other industries. This study aimed to (1) identify sub-gr...

Engineering of Stabilized Ru-Based Proton Exchange Membrane Water Electrolysis.

Proton exchange membrane water electrolysis (PEMWE) is a promising blueprint for the industrial production of green hydrogen in the short term, but it...

A machine learning-based discriminative framework for mild cognitive impairment risk in middle-aged and elderly Chinese patients with depression: evidence from CHARLS and CLHLS.

OBJECTIVE: This study aimed to evaluate the application value of machine learning (ML) techniques in the discrimination of mild cognitive impairment (...

Technology matters: AI-driven tools in children's mental healthcare: perspectives from young people and parents.

Artificial intelligence may be able to improve the efficiency, accuracy and predictiveness of mental health assessments, and public perspectives are c...

Balancing Innovation and Humanism: An Ethical Debate on AI and VR in Psychiatric Nursing Education.

BACKGROUND: Artificial intelligence (AI) and virtual reality (VR) technologies are increasingly integrated into psychiatric nursing education, present...

Symptom trajectories and their predictive factors in psychiatric inpatients with depression.

BACKGROUND: Depression presents with heterogeneity in symptom trajectories, complicating individualized treatment. Identifying distinct classes of sym...

Development of explainable machine learning-based prediction models of depression with mild and severe insomnia in middle-aged and elderly Chinese people.

BACKGROUND: The risk of depression is significantly elevated in middle-aged and older adults with insomnia; however, the pathways between mild and sev...

Resting-state functional connectivity of the fronto-limbic and default mode network as neural correlates of antidepressant response in major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is a leading cause of disability worldwide, yet antidepressant response remains highly variable, with many...

Unconscious elevated bottom-up processing in depression: Insights from dynamic causal modeling with EEG and fMRI.

INTRODUCTION: MRI compatible EEG systems enable simultaneous EEG-fMRI data assessment, which provides high spatial and high temporal resolution of neu...

Individual differences in effects of stressful life events on childhood ADHD: genetic, neural, and familial contributions.

BACKGROUND: This study elucidates the intricate relationship between stressful life events and the development of ADHD symptoms in children, acknowled...

An Internet of Things operating room platform for neurosurgery.

OBJECTIVE: The operating room (OR) is a data-rich environment and largely follows closed-door policies for health data security and privacy. To overco...

A functional system-informed graph neural network framework to quantify interpretable brain dysfunction in ASD.

Autism Spectrum Disorder (ASD) is one of the most common neurodevelopmental disorders affecting patients from childhood to adulthood. Yet, its patholo...

Medication-based mortality prediction in COPD using machine learning and conventional statistical methods.

BACKGROUND: Predicting mortality in chronic obstructive pulmonary disease (COPD) patients supports clinical decision-making and resource allocation. W...

Acoustic and machine learning methods for speech-based suicide risk assessment: A systematic review.

Suicide remains a public health challenge, necessitating improved detection methods to facilitate timely intervention and treatment. This systematic r...

Machine learning prediction of suicide attempts in major depression: Feature selection and model development using national survey data.

BACKGROUND: The study aimed to explore the risk factors of suicidal attempts (SA) in patients with major depressive disorders (MDD). METHODS: Cross-se...

Demonstrating the potential of untargeted hair proteomics for personalized biomarkers in stress-associated disorders.

Biomarker research in psychopathology increasingly employs high-dimensional Omics approaches. Yet, proteomics based on human hair remain largely unexp...