Psychiatry

Causes of death in individuals with lifetime major depression: a comprehensive machine learning analysis from a community-based autopsy center.

BACKGROUND: Depression can be associated with increased mortality and morbidity, but no studies have investigated the specific causes of death based on autopsy reports. Autopsy studies can yield valuable and detailed information on pathological ailments or underreported conditions. This study aimed to compare autopsy-confirmed causes of death (CoD) between individuals diagnosed with major depressi...

Using natural language processing to facilitate the harmonisation of mental health questionnaires: a validation study using real-world data.

BACKGROUND: Pooling data from different sources will advance mental health research by providing larger sample sizes and allowing cross-study comparisons; however, the heterogeneity in how variables are measured across studies poses a challenge to this process.

AI Hesitancy and Acceptability-Perceptions of AI Chatbots for Chronic Health Management and Long COVID Support: Survey Study.

BACKGROUND: Artificial intelligence (AI) chatbots have the potential to assist individuals with chronic health conditions by providing tailored inform...

The acceptance of the potential use of social robots for children with autism spectrum disorder by Indonesian occupational therapists: a mixed methods study.

PURPOSE: Social robots have shown positive effects in treating children with autism spectrum disorder. The development of social robots in Indonesia h...

Constructing prediction models and analyzing factors in suicidal ideation using machine learning, focusing on the older population.

Suicide among the older population is a significant public health concern in South Korea. As the older individuals have long considered suicide before...

Anxiety in young people: Analysis from a machine learning model.

The study addresses the detection of anxiety symptoms in young people using artificial intelligence models. Questionnaires such as the Patient Health ...

The Use of fMRI Regional Analysis to Automatically Detect ADHD Through a 3D CNN-Based Approach.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by a reduced attention span, hyperactivity, and impulsi...

Machine Learning-Based Evaluation of Suicide Risk Assessment in Crisis Counseling Calls.

OBJECTIVE: Counselor assessment of suicide risk is one key component of crisis counseling, and standards require risk assessment in every crisis couns...

Differentiating loss of consciousness causes through artificial intelligence-enabled decoding of functional connectivity.

Differential diagnosis of acute loss of consciousness (LOC) is crucial due to the need for different therapeutic strategies despite similar clinical p...

Identifying Depression Through Machine Learning Analysis of Omics Data: Scoping Review.

BACKGROUND: Depression is one of the most common mental disorders that affects >300 million people worldwide. There is a shortage of providers trained...

Emergence of Artificial Intelligence Art Therapies (AIATs) in Mental Health Care: A Systematic Review.

The application of artificial intelligence art therapies (AIATs) in mental health care represents an innovative merger between digital technology and ...

In Silico drug repurposing pipeline using deep learning and structure based approaches in epilepsy.

Due to considerable global prevalence and high recurrence rate, the pursuit of effective new medication for epilepsy treatment remains an urgent and s...

Decoding depression: a comprehensive multi-cohort exploration of blood DNA methylation using machine learning and deep learning approaches.

The causes of depression are complex, and the current diagnosis methods rely solely on psychiatric evaluations with no incorporation of laboratory bio...

Detecting depression severity using weighted random forest and oxidative stress biomarkers.

This study employs machine learning to detect the severity of major depressive disorder (MDD) through binary and multiclass classifications. We compar...

[Relationship between certain uses of artificial intelligence and psychosocial risk factors in European work environments].

INTRODUCTION: To examine the relationship between the use of Artificial Intelligence (AI) to assess and monitor job performance and exposure to psycho...

Detecting outliers in case-control cohorts for improving deep learning networks on Schizophrenia prediction.

This study delves into the intricate genetic and clinical aspects of Schizophrenia, a complex mental disorder with uncertain etiology. Deep Learning (...

Speech-based recognition and estimating severity of PTSD using machine learning.

BACKGROUND: Traditional methodologies for diagnosing post-traumatic stress disorder (PTSD) primarily rely on interviews, incurring considerable costs ...

Identification and diagnosis of schizophrenia based on multichannel EEG and CNN deep learning model.

This paper proposes a high-accuracy EEG-based schizophrenia (SZ) detection approach. Unlike comparable literature studies employing conventional machi...

Role of different omics data in the diagnosis of schizophrenia disorder: A machine learning study.

Schizophrenia is a serious mental disorder that affects millions of people worldwide. This disorder slowly disintegrates thinking ability and changes ...

Functional Near-Infrared Spectroscopy-Based Computer-Aided Diagnosis of Major Depressive Disorder Using Convolutional Neural Network with a New Channel Embedding Layer Considering Inter-Hemispheric Asymmetry in Prefrontal Hemodynamic Responses.

BACKGROUND: Functional near-infrared spectroscopy (fNIRS) is being extensively explored as a potential primary screening tool for major depressive dis...