Psychiatry

Machine Learning Accurately Predicts Patient-reported Outcomes 1 Year After Breast Reconstruction.

OBJECTIVE: To develop and evaluate machine learning algorithms for predicting patient-reported outcomes following breast reconstruction. BACKGROUND: Machine learning may inform patient-reported outcomes in breast reconstruction, possibly enhancing shared decision-making and tailoring patient care. METHODS: Data on patient characteristics, reconstructive technique, and BREAST-Q scores from women un...

Involvement of disulfidptosis in the pathophysiology of autism spectrum disorder.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder, with oxidative stress recognized as a key pathogenic mechanisms. Oxidative stress disrupts intracellular dynamic- thiol/disulfide homeostasis (DTDH), potentially leading to disulfidptosis, a newly identified cell death mechanism. While studies suggest a link between DTDH and ASD, direct evidence implicating disulfidptosis in ...

Preprocessing of natural language process variables using a data-driven method improves the association with suicide risk in a large veterans affairs population.

OBJECTIVE: Suicide risk assessment has historically relied heavily on clinical evaluations and patient self-reports. Natural language processing (NLP)...

A machine learning approach to predict treatment efficacy and adverse effects in major depression using CYP2C19 and clinical-environmental predictors.

BACKGROUND: Major depressive disorder (MDD) is among the leading causes of disability worldwide and treatment efficacy is variable across patients. Po...

Real-time monitoring to predict depressive symptoms: study protocol.

INTRODUCTION: According to the World Health Organization, Depression is the fourth leading cause of global disease burden. However, traditional clinic...

Initial study of verbal and nonverbal communication training through the collaborative operation of a humanoid robot for individuals with autism spectrum disorder.

Individuals with autism spectrum disorder (ASD) experience difficulties in both verbal and nonverbal communication. Collaborative work allows them to ...

Circadian rhythm modulation in heart rate variability as potential biomarkers for major depressive disorder: A machine learning approach.

Major depressive disorder (MDD) is associated with reduced heart rate variability (HRV), but its link to circadian rhythm modulation (CRM) of HRV is u...

Assessment of PTSD in military personnel via machine learning based on physiological habituation in a virtual immersive environment.

Posttraumatic stress disorder (PTSD) is a complex mental health condition triggered by exposure to traumatic events that leads to physical health prob...

miRNA-Based Diagnosis of Schizophrenia Using Machine Learning.

Diagnostic practices for schizophrenia are unreliable due to the lack of a stable biomarker. However, machine learning holds promise in aiding in the ...

Early Identification of Autism Using Cry Analysis: A Systematic Review and Meta-analysis of Retrospective and Prospective Studies.

Cry analysis is emerging as a promising tool for early autism identification. Acoustic features such as fundamental frequency (F0), cry duration, and ...

Bootstrap inference and machine learning reveal core differential plasma metabolic connectome signatures in major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) involves molecular alterations and pathway dysregulation. Metabolic interconnections are crucial for norma...

Graphene-based FETs for advanced biocatalytic profiling: investigating heme peroxidase activity with machine learning insights.

Graphene-based field-effect transistors (GFETs) are rapidly gaining recognition as powerful tools for biochemical analysis due to their exceptional se...

The role of senescence-related genes in major depressive disorder: insights from machine learning and single cell analysis.

BACKGROUND: Evidence indicates that patients with Major Depressive Disorder (MDD) exhibit a senescence phenotype or an increased susceptibility to pre...

Development and Feasibility Study of HOPE Model for Prediction of Depression Among Older Adults Using Wi-Fi-based Motion Sensor Data: Machine Learning Study.

BACKGROUND: Depression, characterized by persistent sadness and loss of interest in daily activities, greatly reduces quality of life. Early detection...

The Extent to Which Artificial Intelligence Can Help Fulfill Metastatic Breast Cancer Patient Healthcare Needs: A Mixed-Methods Study.

The Artificial Intelligence Patient Librarian (AIPL) was designed to meet the psychosocial and supportive care needs of Metastatic Breast Cancer (MBC)...

Corrigendum to "A case for the use of deep learning algorithms for individual and population level assessments of mental health disorders: Predicting depression among China's elderly" [J. Affect. Disord. Volume 369, 15 January 2025, Pages 329-337].

Digital Horizons: Enhancing Autism Support with Augmented Reality.

PURPOSE: This paper aims to comprehensively review the application of Augmented Reality interventions in supporting individuals diagnosed with Autism ...

Comprehensive evaluation of pipelines for classification of psychiatric disorders using multi-site resting-state fMRI datasets.

Objective classification biomarkers that are developed using resting-state functional magnetic resonance imaging (rs-fMRI) data are expected to contri...

Psychiatric Genomics 2025: State of the Art and the Path Forward.

Psychiatric genetics has evolved from candidate-gene studies to whole-genome sequencing efforts. With hundreds of disease-associated loci now identifi...

The use of artificial intelligence in psychotherapy: development of intelligent therapeutic systems.

BACKGROUND: The increasing demand for psychotherapy and limited access to specialists underscore the potential of artificial intelligence (AI) in ment...