Depression

A method for predicting postpartum depression via an ensemble neural network model.

INTRODUCTION: Postpartum depression (PPD) has numerous adverse impacts on the families of new mothers and society at large. Early identification and intervention are of great significance. Although there are many existing machine learning classifiers for PPD prediction, the requirements for high accuracy and the interpretability of models present new challenges.

Text-Based Depression Prediction on Social Media Using Machine Learning: Systematic Review and Meta-Analysis.

BACKGROUND: Depression affects more than 350 million people globally. Traditional diagnostic methods have limitations. Analyzing textual data from social media provides new insights into predicting depression using machine learning. However, there is a lack of comprehensive reviews in this area, which necessitates further research.

Semantic signals in self-reference: The detection and prediction of depressive symptoms from the daily diary entries of a sample with major depressive disorder.

Individuals with major depressive disorder (MDD) experience fewer positive and more negative emotions and use fewer positive words to describe themsel...

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Introducción: la ideación suicida es el pensamiento de autoeliminación no siempre reportada por los pacientes en test de depresión. El objetivo fue id...

Evaluating the agreement between ChatGPT-4 and validated questionnaires in screening for anxiety and depression in college students: a cross-sectional study.

BACKGROUND: The Chat Generative Pre-trained Transformer (ChatGPT), an artificial intelligence-based web application, has demonstrated substantial pote...

The Association of Elevated Depression Levels and Life's Essential 8 on Cardiovascular Health With Predicted Machine Learning Models and Interpretations: Evidence From NHANES 2007-2018.

The association between depression severity and cardiovascular health (CVH) represented by Life's Essential 8 (LE8) was analyzed, with a novel focus ...

Enhancing student-centered walking environments on university campuses through street view imagery and machine learning.

Campus walking environments significantly influence college students' daily lives and shape their subjective perceptions. However, previous studies ha...

Prediction of first attempt of suicide in early adolescence using machine learning.

BACKGROUND: Suicide is the second leading cause of death among early adolescents, yet the first onset of suicide attempts during this critical develop...

Greenspace and depression incidence in the US-based nationwide Nurses' Health Study II: A deep learning analysis of street-view imagery.

BACKGROUND: Greenspace exposure is associated with lower depression risk. However, most studies have measured greenspace exposure using satellite-base...

Direct-acting antivirals (DAA) positively affect depression and cognitive function in patients with chronic hepatitis C.

The aim of the study was to determine how depression and cognitive dysfunction in patients with chronic hepatitis C virus (HCV) infection are affected...

Estimating depression severity in narrative clinical notes using large language models.

BACKGROUND: Depression treatment guidelines emphasize measurement-based care using patient-reported outcome measures, yet their impact on narrative do...

Photoplethysmography-based HRV analysis and machine learning for real-time stress quantification in mental health applications.

Prolonged exposure to high-stress environments can lead to mental illnesses such as anxiety disorders, depression, and posttraumatic stress disorder. ...

Effect of a ChatGPT-based digital counseling intervention on anxiety and depression in patients with cancer: A prospective, randomized trial.

BACKGROUND: Psychological distress is prevalent among newly diagnosed cancer patients, often exacerbating treatment-related anxiety and depression. Ar...

Gene age gap estimate (GAGE) for major depressive disorder: A penalized biological age model using gene expression.

Recent associations between Major Depressive Disorder (MDD) and measures of premature aging suggest accelerated biological aging as a potential biomar...

Multivariate Classification of Adolescent Major Depressive Disorder Using Whole-brain Functional Connectivity.

RATIONALE AND OBJECTIVES: Adolescent major depressive disorder (MDD) is a serious mental health condition that has been linked to abnormal functional ...

The interpretable machine learning model for depression associated with heavy metals via EMR mining method.

Limited research exists on the association between depression and heavy metal exposure. This study aims to develop an interpretable and efficient mach...

Fall recognition using a three stream spatio temporal GCN model with adaptive feature aggregation.

The prevention of falls is paramount in modern healthcare, particularly for the elderly, as falls can lead to severe injuries or even fatalities. Addi...

Exploring the Potential of Large Language Models for Automated Safety Plan Scoring in Outpatient Mental Health Settings.

The Safety Planning Intervention (SPI) produces a plan to help manage patients' suicide risk. High-quality safety plans - that is, those with greater ...

Machine learning-driven development of a stratified CES-D screening system: optimizing depression assessment through adaptive item selection.

OBJECTIVE: To develop a stratified screening tool through machine learning approaches for the Center for Epidemiologic Studies Depression Scale (CES-D...

Network-based predictive models for artificial intelligence: an interpretable application of machine learning techniques in the assessment of depression in stroke patients.

BACKGROUND: Depression is a common complication after a stroke that may lead to increased disability and decreased quality of life. The objective of t...