Psychiatry

Multimodal machine learning for major depressive disorder: Integrating EEG functional connectivity and clinical variables to enhance diagnostic accuracy.

The diagnosis of Major Depressive Disorder (MDD) relies heavily on subjective clinical assessments. This study evaluated various machine learning models in differentiating between MDD patients and healthy controls using resting-state electroencephalography (EEG) features and clinical variables as input variables. A total of 123 participants, including 77 MDD patients and 46 sex- and age-matched co...

A dual-branch deep learning framework for emotion recognition from EEG signals.

Mental health monitoring through emotion recognition plays an important role in early intervention and personalized healthcare systems. Traditional EEG-based emotion recognition approaches have encountered significant limitations, including heavy reliance on manual feature engineering, poor generalization across datasets, and computational complexity that restricts real-world deployment. This stud...

Stack-Engineered Mode Selection in PtMn/(Co/Pd)n Multilayers Enables Deterministic Analog Spin-Orbit Torque Synapses.

Spin-orbit-torque (SOT) devices that support both binary and analog switching can bridge spintronic memory and neuromorphic computing, provided the sw...

Mortality score in chronic coronary syndrome: prediction model from the ISCHEMIA trial.

AIMS: Accurate stratification of mortality risk is essential for management of chronic coronary syndromes (CCS), but existing models focus primarily o...

Construction and validation of a predictive model for suicidal attempts in patients with mood disorders after discharge based on machine learning methods: A two-year follow-up study.

BACKGROUND: Suicide attempts (SA) in patients with mood disorders (MD) should be paid enough attention. Currently, there is a lack of relevant researc...

Classification of functional near-infrared spectroscopy (fNIRS) signals in schizophrenia and bipolar disorder using deep learning methods.

BACKGROUND: Schizophrenia (SCZ) and Bipolar Disorder (BD) are prevalent mental disorders. This study uses functional near-infrared spectroscopy (fNIRS...

A SEM-ANN analysis to examine impact of AI overreliance through a social cognitive lens: the roles of dependency, FOMO, addiction, and anxiety.

The rapid integration of artificial intelligence (AI) has transformed how people learn, work, and make decisions, while raising growing concerns about...

Machine learning-based prediction model for fall risk among individuals with arthritis in China: an analysis using the China Health and Retirement Longitudinal Study (CHARLS) database.

BACKGROUND: Falling has become a global public health problem. Individuals with arthritis have a higher risk of falling because of joint pain, poor mu...

The Performance of Wearable Device-Based Artificial Intelligence in Detecting Depression: Systematic Review and Meta-Analysis.

BACKGROUND: In recent years, advances in wearable sensor technology and artificial intelligence (AI) have provided new possibilities for detecting and...

EEG based detection of schizophrenia using asymmetry of entropy and CNN-LSTM model.

Schizophrenia is a severe neuropsychiatric disorder with a significant impact on individual's real-life functioning. It is characterized by abnormal a...

Functional brain network modulation following conventional and optimized HD-tDCS in major depressive disorder: A machine learning prediction of treatment response.

BACKGROUND: Major depressive disorder (MDD) is a prevalent and disabling condition that remains inadequately treated in many patients. Transcranial di...

DisNet : Learning interpretable depression representations in speech.

Speech-based depression detection (SDD) offers an objective and convenient method for depression screening and intervention. However, existing deep le...

Application of artificial intelligence in schizophrenia rehabilitation management: a systematic scoping review.

Artificial intelligence (AI) is increasingly used in mental health, yet its rehabilitation-oriented applications in schizophrenia have not been system...

Deep Brain Stimulation Induces Antidepressant Effects by Restoring High-Fidelity Communication in the BNST-NAc Circuit.

Deep brain stimulation (DBS) for treatment-resistant depression (TRD) is challenged by significant individual variability in efficacy and unclear neur...

Association Between Digital Biomarkers of Health and Anxiety: Systematic Review and Meta-Analysis.

BACKGROUND: Digital biomarkers are gaining interest as proxy markers for mental health, as they enable passive and continuous data collection. However...

Machine learning in the analysis of mental health at work: a scoping review.

OBJECTIVES: This scoping review aims to assess the role of machine learning in workplace mental health research by systematically analyzing existing s...

Magnetic resonance imaging study of major depressive disorder: a 28-year scientometric and visual analysis.

Neuroimaging, particularly magnetic resonance imaging (MRI), has become a cornerstone in elucidating the neural underpinnings of Major Depressive Diso...

Effectiveness of meditation in the management of epilepsy: An updated mini systematic review of recent findings.

BACKGROUND: Epilepsy is a chronic neurological disorder characterized by altered cortical excitability. The disorder is often associated with psycholo...

A cautionary tale for AI and machine learning in psychiatry.

Artificial intelligence (AI) and machine learning (ML) have seen remarkable growth in mental health applications over the past few decades, demonstrat...

Machine learning approaches to identifying neurodevelopmental disorders using social media data: a systematic review.

BACKGROUND: Neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), often eme...