Psychiatry

Siamese based deep neural network for ADHD detection using EEG signal.

BACKGROUND: Detecting Attention-Deficit/Hyperactivity Disorder (ADHD) in children is crucial for timely intervention and personalized treatment.

Digital therapeutics in hypertension: How to make sustainable lifestyle changes.

Various digital therapeutic products have been validated and approved since 2017. They have demonstrated efficacy and safety as a new therapeutic modality in various disorders or conditions. Hypertension is a common but serious condition that can be prevented or controlled with lifestyle changes and medicines. Although a digital therapeutic in hypertension is validated and approved in Japan, wheth...

Prediction of depressive symptoms severity based on sleep quality, anxiety, and gray matter volume: a generalizable machine learning approach across three datasets.

BACKGROUND: Depressive symptoms are rising in the general population, but their associated factors are unclear. Although the link between sleep distur...

The Role of Complicated Grief in Health Inequities in American Indian Communities.

Complicated grief is both a cause and a consequence of health inequities in Native (American Indian/Alaska Native [AI/AN]) communities. Given dispropo...

Identification of Immune-Related Biomarkers of Schizophrenia in the Central Nervous System Using Bioinformatic Methods and Machine Learning Algorithms.

Schizophrenia is a disastrous mental disorder. Identification of diagnostic biomarkers and therapeutic targets is of significant importance. In this s...

Analysis of coupling in geographic information systems based on WASPAS method for bipolar complex fuzzy linguistic Aczel-Alsina power aggregation operators.

The model of bipolar complex fuzzy linguistic set is a very famous and dominant principle to cope with vague and uncertain information. The bipolar co...

Suicidal behaviors among high school graduates with preexisting mental health problems: A machine learning and GIS-based study.

BACKGROUND: Suicidal behavior among adolescents with mental health disorders, such as depression and anxiety, is a critical issue. This study explores...

Risk factors for depression in China based on machine learning algorithms: A cross-sectional survey of 264,557 non-manual workers.

BACKGROUND: Factors related to depression differ depending on the population studied, and studies focusing on the population of non-manual workers are...

Prediction of anhedonia in patients with first-episode schizophrenia using a Wavelet-ALFF-based Support vector regression model.

Anhedonia is one of the core features of the negative symptoms of schizophrenia and can be extremely burdensome. Our study applied resting-state funct...

EEGDepressionNet: A Novel Self Attention-Based Gated DenseNet With Hybrid Heuristic Adopted Mental Depression Detection Model Using EEG Signals.

World Health Organization (WHO) has identified depression as a significant contributor to global disability, creating a complex thread in both public ...

HEMAsNet: A Hemisphere Asymmetry Network Inspired by the Brain for Depression Recognition From Electroencephalogram Signals.

Depression is a prevalent mental disorder that affects a significant portion of the global population. Despite recent advancements in EEG-based depres...

Natural Language Processing for Depression Prediction on Sina Weibo: Method Study and Analysis.

BACKGROUND: Depression represents a pressing global public health concern, impacting the physical and mental well-being of hundreds of millions worldw...

Social anxiety prediction based on ERP features: A deep learning approach.

BACKGROUND: Social Anxiety Disorder is traditionally diagnosed using subjective scales that may lack accuracy. Recently, EEG technology has gained imp...

The challenges of using machine learning models in psychiatric research and clinical practice.

To understand the complex nature of heterogeneous psychiatric disorders, scientists and clinicians are required to employ a wide range of clinical, en...

Protocol for Designing a Model to Predict the Likelihood of Psychosis From Electronic Health Records Using Natural Language Processing and Machine Learning.

INTRODUCTION: Rapid identification of individuals developing a psychotic spectrum disorder (PSD) is crucial because untreated psychosis is associated ...

Using machine learning models to identify the risk of depression in middle-aged and older adults with frequent and infrequent nicotine use: A cross-sectional study.

BACKGROUND: Depression is very prevalent in middle-aged and older smokers. Therefore, we aimed to identify the risk of depression among middle-aged an...

Fractional whale driving training-based optimization enabled transfer learning for detecting autism spectrum disorder.

Autism Spectrum Disorder (ASD) is a neurological illness that degrades communication and interaction among others. Autism can be detected at any stage...

Forecasting the incidence frequencies of schizophrenia using deep learning.

Mental disorders are becoming increasingly prevalent worldwide, and accurate incidence forecasting is crucial for effective mental health strategies. ...

Machine Learning Model Reveals Determinators for Admission to Acute Mental Health Wards From Emergency Department Presentations.

This research addresses the critical issue of identifying factors contributing to admissions to acute mental health (MH) wards for individuals present...

DepressionEmo: A novel dataset for multilabel classification of depression emotions.

Emotions are integral to human social interactions, with diverse responses elicited by various situational contexts. Particularly, the prevalence of n...