Depression

Interpretable Machine Learning Model for Predicting Postpartum Depression: Retrospective Study.

BACKGROUND: Postpartum depression (PPD) is a prevalent mental health issue with significant impacts on mothers and families. Exploring reliable predictors is crucial for the early and accurate prediction of PPD, which remains challenging.

Machine learning-based assessment of morphometric abnormalities distinguishes bipolar disorder and major depressive disorder.

INTRODUCTION: Bipolar disorder (BD) and major depressive disorder (MDD) have overlapping clinical presentations which may make it difficult for clinicians to distinguish them potentially resulting in misdiagnosis. This study combined structural MRI and machine learning techniques to determine whether regional morphological differences could distinguish patients with BD and MDD.

Machine learning-based prediction of illness course in major depression: The relevance of risk factors.

BACKGROUND: Major depressive disorder (MDD) comes along with an increased risk of recurrence and poor course of illness. Machine learning has recently...

Dynamic Control of Weight-Update Linearity in Magneto-Ionic Synapses.

Multifunctional hardware technologies for neuromorphic computing are essential for replicating the complexity of biological neural systems, thereby im...

Natural language processing to identify suicidal ideation and anhedonia in major depressive disorder.

BACKGROUND: Anhedonia and suicidal ideation are symptoms of major depressive disorder (MDD) that are not regularly captured in structured scales but m...

Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning.

Major depressive disorder (MDD) is associated with substantial morbidity and mortality, yet its diagnosis and treatment rates remain low due to its di...

Exploring the triglyceride-glucose index's role in depression and cognitive dysfunction: Evidence from NHANES with machine learning support.

BACKGROUND: Depression and cognitive impairments are prevalent among older adults, with evidence suggesting potential links to obesity and lipid metab...

Application of functional near-infrared spectroscopy and machine learning to predict treatment response after six months in major depressive disorder.

Depression treatment responses vary widely among individuals. Identifying objective biomarkers with predictive accuracy for therapeutic outcomes can e...

Application of machine learning in depression risk prediction for connective tissue diseases.

This study retrospectively collected clinical data from 480 patients with connective tissue diseases (CTDs) at Nanjing First Hospital between August 2...

Is Artificial Intelligence the Next Co-Pilot for Primary Care in Diagnosing and Recommending Treatments for Depression?

Depression poses significant challenges to global healthcare systems and impacts the quality of life of individuals and their family members. Recent a...

Depression-related innate immune genes and pan-cancer gene analysis and validation.

BACKGROUND: Depression, a prevalent chronic mental disorder, presents complexities and treatment challenges that drive researchers to seek new, precis...

Enhancing prediction of major depressive disorder onset in adolescents: A machine learning approach.

Major Depressive Disorder (MDD) is a prevalent mental health condition that often begins in adolescence, with significant long-term implications. Indi...

Automated classification of stress and relaxation responses in major depressive disorder, panic disorder, and healthy participants via heart rate variability.

BACKGROUND: Stress is a significant risk factor for psychiatric disorders such as major depressive disorder (MDD) and panic disorder (PD). This highli...

Development and validation of a prediction model for coronary heart disease risk in depressed patients aged 20 years and older using machine learning algorithms.

BACKGROUND: Depression is being increasingly acknowledged as an important risk factor contributing to coronary heart disease (CHD). Currently, there i...

Schizophrenia more employable than depression? Language-based artificial intelligence model ratings for employability of psychiatric diagnoses and somatic and healthy controls.

Artificial Intelligence (AI) assists recruiting and job searching. Such systems can be biased against certain characteristics. This results in potenti...

The Goldilocks Zone: Finding the right balance of user and institutional risk for suicide-related generative AI queries.

Generative artificial intelligence (genAI) has potential to improve healthcare by reducing clinician burden and expanding services, among other uses. ...

Classification of female MDD patients with and without suicidal ideation using resting-state functional magnetic resonance imaging and machine learning.

Spontaneous blood oxygen level-dependent signals can be indirectly recorded in different brain regions with functional magnetic resonance imaging. In ...

A Plasma Proteomics-Based Model for Identifying the Risk of Postpartum Depression Using Machine Learning.

Postpartum depression (PPD) poses significant risks to maternal and infant health, yet proteomic analyses of PPD-risk women remain limited. This study...

Prediction of late-onset depression in the elderly Korean population using machine learning algorithms.

Late-onset depression (LOD) refers to depression that newly appears in elderly individuals without prior depression episodes. Predicting future depres...

Machine learning algorithms to predict depression in older adults in China: a cross-sectional study.

OBJECTIVE: The 2-fold objective of this research is to investigate machine learning's (ML) predictive value for the incidence of depression among Chin...