Depression

Insights into middle-aged men (40-70) who died by suicide: a nationwide mixed methods study in the Netherlands.

BACKGROUND: Globally, middle-aged men dominate suicide statistics in many regions. To understand their heightened vulnerability, we examine the interplay between sociodemographic factors, contextualized through psychosocial perspectives. METHODS: Data from Statistics Netherlands of all middle-aged men (40-70) who died by suicide between 2012 and 2021 (N = 6,656) and interview data from a psychosoc...

Predicting adolescent suicidal tendency in Chinese secondary school students: a machine learning approach with XGBoost and SHAP interpretation.

BACKGROUND: Adolescent suicide is a critical public health issue globally. Early detection of suicidal tendency remains challenging due to its concealed and multidimensional nature. This study aimed to develop and validate an interpretable machine learning model to predict suicidal tendency among Chinese secondary school students. METHODS: A cross-sectional survey was conducted among 12,063 studen...

Generative Artificial Intelligence in the Lives of Young Adults: Exploring Motivations and Mental Health.

PURPOSE: Young people have rapidly adopted generative artificial intelligence (genAI) technology, yet little is known about how genAI use relates to m...

Depression-Related Nutritional Risk and Physical Performance in Middle- to Older-Aged Adults: Cross-Sectional Secondary Analysis of Tree-Based and Regression Approaches.

BACKGROUND: Depressive symptoms are linked to nutritional vulnerability and functional decline in aging populations, but their relationships with nutr...

A Preliminary Open-Label Pilot Study of a Pillow-Based ELF-ELME (Extremely Low-Frequency, Extremely Low Magnetic Environment) Intervention for Major Depressive Disorder.

BACKGROUND: Mitochondrial dysfunction has been implicated in the pathogenesis of depression. Major depressive disorder (MDD) is a prevalent condition ...

Using Machine Learning to Analyze the Predictors of Life Satisfaction: Focus on Lifestyle Attitudes and Psychological Factors.

OBJECTIVES: Life satisfaction is an essential indicator of quality of life, and enhancing it can contribute to individual well-being strategies. Becau...

Mental Health Risk Detection From Social Media Text Data: A Scoping Review of the Machine Learning Research Landscape.

Machine learning approaches have been increasingly applied to social media text data for mental health risk detection. However, existing studies vary ...

Predicting Depression Risk in Physically Inactive Older Adults Using Dietary Antioxidants and Machine Learning: A SHAP-Interpretable Analysis of NHANES.

BACKGROUND: Physically inactive older adults represent a high-risk group for depression. However, whether dietary antioxidant intake profiles can help...

Predicting depressive symptoms among Chinese college students using recurrent neural networks with longitudinal data.

College students face a higher risk of depression than their non-college peers. However, the predictors of depressive symptoms among college students ...

Predicting risk of mental health deterioration using multimodal data from the UK biobank.

INTRODUCTION: Prospective prediction of mental health risk is critical for early intervention to reduce the burden of depression, anxiety, and cogniti...

Innovative teaching in legal medicine through forensic simulation: a case-based educational project.

In Italy, forensic medicine education is traditionally theory-based, which limits students' access to practical experiences due to confidentiality con...

Machine learning-based identification of multidimensional predictors of quality of life in individuals with multiple sclerosis.

This study aimed to identify independent predictors of quality of life in patients with multiple sclerosis (MS) using machine learning approaches. One...

Effectiveness of AI and rule-based conversational agents for depression, anxiety and stress: A meta-analysis.

Depression, anxiety, and stress are significant global health burdens worsened by restricted access to care. Conversational agents (CAs), encompassing...

Large language model empowered explainable and interpretable mental health analysis.

Concern about expressing depressive symptoms on social media is growing in the digital age. Traditional detection methods can identify depression but ...

Joint trajectories of sleep duration and depressive symptoms and risk of incident multimorbidity: a longitudinal analysis with machine learning prediction.

BACKGROUND: Sleep disturbances and depressive symptoms frequently co-occur in older adults. Both conditions follow distinct, time-varying trajectories...

Displaced minds: ecologies of depression.

This issue of the Biomedical Journal explores emerging perspectives across neuropsychiatry, metabolism, aging, regenerative medicine, artificial intel...

Gene-Environment Interactions in Predicting Self-Harm: A Machine Learning Approach Using Explainable Artificial Intelligence.

BACKGROUND: Self-harm is a critical public health concern; nevertheless, the complex interplay between genetic predispositions and environmental facto...

Psychological distress in cancer survivors: a population-based analysis and machine learning-based risk stratification.

PURPOSE: As the number of cancer survivors increases, psychological distress has become an important issue. Using nationally representative data, we e...

Crystallization-Driven Stable Resistive Switching and Reproducible Synaptic Learning in GeSe-Based Artificial Synapses.

The rising demand for high-performance, energy-efficient neuromorphic systems has driven the exploration of chalcogenide materials with tunable electr...

Early identification of major depression risk among reproductive-age women using hormonal contraceptives in southern Ethiopia: A Machine Learning Approach.

Machine-learning (ML) algorithms are increasingly valuable in health sciences because they can analyze complex, high-dimensional data and detect patte...