Depression

Temporal dynamic alterations of regional homogeneity in major depressive disorder: a study integrating machine learning.

Previous studies have found alterations in the local regional homogeneity of brain activity in individuals diagnosed with major depressive disorder. However, many studies have failed to consider that even during resting states, brain activity is dynamic and time-varying. The lack of investigation into the dynamic regional homogeneity has hindered the discovery of biomarkers for depression. This st...

Global Suicide Mortality Rates (2000-2019): Clustering, Themes, and Causes Analyzed through Machine Learning and Bibliographic Data.

Suicide research is directed at understanding social, economic, and biological causes of suicide thoughts and behaviors. (1) Background: Worldwide, certain countries have high suicide mortality rates (SMRs) compared to others. Age-standardized suicide mortality rates (SMRs) published by the World Health Organization (WHO) plus numerous bibliographic records of the Web of Science (WoS) database pro...

Deep Learning-Based Artificial Intelligence Can Differentiate Treatment-Resistant and Responsive Depression Cases with High Accuracy.

Although there are many treatment options available for depression, a large portion of patients with depression are diagnosed with treatment-resistan...

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...

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...

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...

Self-Administered Interventions Based on Natural Language Processing Models for Reducing Depressive and Anxiety Symptoms: Systematic Review and Meta-Analysis.

BACKGROUND: The introduction of natural language processing (NLP) technologies has significantly enhanced the potential of self-administered intervent...

Breaking the silence: leveraging social interaction data to identify high-risk suicide users online using network analysis and machine learning.

Suicidal thought and behavior (STB) is highly stigmatized and taboo. Prone to censorship, yet pervasive online, STB risk detection may be improved thr...

Trajectory on postpartum depression of Chinese women and the risk prediction models: A machine-learning based three-wave follow-up research.

BACKGROUND: Our study delves into postpartum depression (PPD) extending observation up to six months postpartum, addressing the gap in long-term follo...

Spatiotemporal discoordination of brain spontaneous activity in major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is a widespread mental health issue, impacting spatial and temporal aspects of brain activity. The neural ...

Using Data-Driven Algorithms with Large-Scale Plasma Proteomic Data to Discover Novel Biomarkers for Diagnosing Depression.

Given recent technological advances in proteomics, it is now possible to quantify plasma proteomes in large cohorts of patients to screen for biomarke...

Identifying neuroimaging biomarkers in major depressive disorder using machine learning algorithms and functional near-infrared spectroscopy (fNIRS) during verbal fluency task.

One of the most prevalent psychiatric disorders is major depressive disorder (MDD), which increases the probability of suicidal ideation or untimely d...

The metabolic clock of ketamine abuse in rats by a machine learning model.

Ketamine has recently become an anesthetic drug used in human and veterinary clinical medicine for illicit abuse worldwide, but the detection of illic...

Predicting the trajectory of non-suicidal self-injury among adolescents.

BACKGROUND: Non-suicidal self-injury (NSSI) is common among adolescents receiving inpatient psychiatric treatment and the months post-discharge is a h...