AIMC Topic: Depressive Disorder, Major
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Machine learning-based assessment of morphometric abnormalities distinguishes bipolar disorder and major depressive disorder.
Neuroradiology
Jan 18, 2025
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 a...
Machine learning-based prediction of illness course in major depression: The relevance of risk factors.
Journal of affective disorders
Jan 14, 2025
BACKGROUND: Major depressive disorder (MDD) comes along with an increased risk of recurrence and poor course of illness. Machine learning has recently shown promise in the prediction of mental illness, yet models aiming to predict MDD course are stil...
Natural language processing to identify suicidal ideation and anhedonia in major depressive disorder.
BMC medical informatics and decision making
Jan 13, 2025
BACKGROUND: Anhedonia and suicidal ideation are symptoms of major depressive disorder (MDD) that are not regularly captured in structured scales but may be captured in unstructured clinical notes. Natural language processing (NLP) techniques may be u...
Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning.
Sensors (Basel, Switzerland)
Jan 12, 2025
Major depressive disorder (MDD) is associated with substantial morbidity and mortality, yet its diagnosis and treatment rates remain low due to its diverse and often overlapping clinical manifestations. In this context, electroencephalography (EEG) h...
Application of functional near-infrared spectroscopy and machine learning to predict treatment response after six months in major depressive disorder.
Translational psychiatry
Jan 11, 2025
Depression treatment responses vary widely among individuals. Identifying objective biomarkers with predictive accuracy for therapeutic outcomes can enhance treatment efficiency and avoid ineffective therapies. This study investigates whether functio...
Enhancing prediction of major depressive disorder onset in adolescents: A machine learning approach.
Journal of psychiatric research
Jan 9, 2025
Major Depressive Disorder (MDD) is a prevalent mental health condition that often begins in adolescence, with significant long-term implications. Indicated prevention programs targeting adolescents with mild symptoms have shown efficacy, yet the meth...
Prediction of late-onset depression in the elderly Korean population using machine learning algorithms.
Scientific reports
Jan 7, 2025
Late-onset depression (LOD) refers to depression that newly appears in elderly individuals without prior depression episodes. Predicting future depression is crucial for mitigating the risk of major depression in prospective patients. This study aims...
Diagnosis of major depressive disorder using a novel interpretable GCN model based on resting state fMRI.
Neuroscience
Dec 25, 2024
The diagnosis and analysis of major depressive disorder (MDD) faces some intractable challenges such as dataset limitations and clinical variability. Resting-state functional magnetic resonance imaging (Rs-fMRI) can reflect the fluctuation data of br...
Momentary Depression Severity Prediction in Patients With Acute Depression Who Undergo Sleep Deprivation Therapy: Speech-Based Machine Learning Approach.
JMIR mental health
Dec 23, 2024
BACKGROUND: Mobile devices for remote monitoring are inevitable tools to support treatment and patient care, especially in recurrent diseases such as major depressive disorder. The aim of this study was to learn if machine learning (ML) models based ...
Model-informed approach to estimate treatment effect in placebo-controlled clinical trials using an artificial intelligence-based propensity weighting methodology to account for non-specific responses to treatment.
Journal of pharmacokinetics and pharmacodynamics
Dec 10, 2024
In randomized, placebo controlled clinical trials (RCT) in major depressive disorders (MDD), treatment response (TR) is estimated by the change from baseline at study-end (EOS) of the scores of clinical scales used for assessing disease severity. Tre...
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