Psychiatry

Development and Validation of Prediction Models for the Diagnosis of Autism Spectrum Disorder in a Korean General Population.

OBJECTIVE: Delays in autism spectrum disorder (ASD) diagnosis and treatment are significant clinical problems that can be addressed by timely, community-based assessment. This study examined tools for identifying ASD in community settings using machine learning (ML) models.

At-home, telehealth-supported ketamine treatment for depression: Findings from longitudinal, machine learning and symptom network analysis of real-world data.

BACKGROUND: Improving safe and effective access to ketamine therapy is of high priority given the growing burden of mental illness. Telehealth-supported administration of sublingual ketamine is being explored toward this goal.

Role of machine learning algorithms in suicide risk prediction: a systematic review-meta analysis of clinical studies.

OBJECTIVE: Suicide is a complex and multifactorial public health problem. Understanding and addressing the various factors associated with suicide is ...

A machine-learning approach for differentiating borderline personality disorder from community participants with brain-wide functional connectivity.

BACKGROUND: Functional connectivity has garnered interest as a potential biomarker of psychiatric disorders including borderline personality disorder ...

An automated approach for predicting HAMD-17 scores via divergent selective focused multi-heads self-attention network.

This study introduces the Divergent Selective Focused Multi-heads Self-Attention Network (DSFMANet), an innovative deep learning model devised to auto...

An Artificial Neural Network Predicts Gender Differences of Motor and Non-Motor Symptoms of Patients with Advanced Parkinson's Disease under Levodopa-Carbidopa Intestinal Gel.

: Currently, no tool exists to predict clinical outcomes in patients with advanced Parkinson's disease (PD) under levodopa-carbidopa intestinal gel (L...

Lack of evidence for predictive utility from resting state fMRI data for individual exposure-based cognitive behavioral therapy outcomes: A machine learning study in two large multi-site samples in anxiety disorders.

Data-based predictions of individual Cognitive Behavioral Therapy (CBT) treatment response are a fundamental step towards precision medicine. Past stu...

Effects of midwifery and nursing students' readiness about medical Artificial intelligence on Artificial intelligence anxiety.

BACKGROUND: Artificial intelligence technologies are one of the most important technologies of today. Developments in artificial intelligence technolo...

Temporal prediction of suicidal ideation in an ecological momentary assessment study with recurrent neural networks.

INTRODUCTION: Ecological Momentary Assessment (EMA) holds promise for providing insights into daily life experiences when studying mental health pheno...

A machine learning model to predict the risk of perinatal depression: Psychosocial and sleep-related factors in the Life-ON study cohort.

Perinatal depression (PND) is a common complication of pregnancy associated with serious health consequences for both mothers and their babies. Identi...

Development and validation of a machine learning model for prediction of comorbid major depression disorder among narcolepsy type 1.

BACKGROUND: Major depression disorder (MDD) forms a common psychiatric comorbidity among patients with narcolepsy type 1 (NT1), yet its impact on pati...

The Artificial Third: A Broad View of the Effects of Introducing Generative Artificial Intelligence on Psychotherapy.

This paper explores a significant shift in the field of mental health in general and psychotherapy in particular following generative artificial intel...

Predicting first time depression onset in pregnancy: applying machine learning methods to patient-reported data.

PURPOSE: To develop a machine learning algorithm, using patient-reported data from early pregnancy, to predict later onset of first time moderate-to-s...

MicroRNA classification and discovery for major depressive disorder diagnosis: Towards a robust and interpretable machine learning approach.

BACKGROUND: Major depressive disorder (MDD) is notably underdiagnosed and undertreated due to its complex nature and subjective diagnostic methods. Bi...

Construction of an antidepressant priority list based on functional, environmental, and health risks using an interpretable mixup-transformer deep learning model.

As emerging pollutants, antidepressants (AD) must be urgently investigated for risk identification and assessment. This study constructed a comprehens...

Evaluation of EEG Signals by Spectral Peak Methods and Statistical Correlation for Mental State Discrimination Induced by Arithmetic Tasks.

Bringing out brain activity through the interpretation of EEG signals is a challenging problem that involves combined methods of signal analysis. The ...

A novel machine learning-based prediction method for patients at risk of developing depressive symptoms using a small data.

The prediction of depression is a crucial area of research which makes it one of the top priorities in mental health research as it enables early inte...

The Importance of Being Consistent: Attribution of Mental States in Strategic Human-Robot Interactions.

This article investigates the attribution of mental state (AMS) to an anthropomorphic robot by humans in a strategic interaction. We conducted an expe...

Identifying Cardiovascular Disease Risk Endotypes of Adolescent Major Depressive Disorder Using Exploratory Unsupervised Machine Learning.

OBJECTIVE: Adolescents with major depressive disorder (MDD) are at increased risk of premature atherosclerosis and cardiovascular disease (CVD). The a...

Machine-learning-based feature selection to identify attention-deficit hyperactivity disorder using whole-brain white matter microstructure: A longitudinal study.

BACKGROUND: We aimed to identify important features of white matter microstructures collectively distinguishing individuals with attention-deficit/hyp...