Depression

Enhancing differentiation between unipolar and bipolar depression through integration of machine learning and electroencephalogram analysis.

To enhance the differentiation between unipolar depression (UPD) and bipolar depression (BPD), this study integrates machine learning and deep learning models with electroencephalography (EEG) data and clinical features. Utilizing Python for data preprocessing and feature extraction, we analyzed 370 patients diagnosed with either UPD or BPD. The experimental design featured 5-fold cross-validation...

Machine learning-based prediction of respiratory depression during sedation for liposuction.

Procedural sedation is often performed by non-anesthesiologists in various settings and can lead to respiratory depression. A tool that enables early detection of respiratory compromise could not only enhance patient safety during procedural sedation, but also reduce the risk of medical liability. In this study, we aimed to develop a machine learning model that integrates detailed body composition...

Prediction of depression risk in middle-aged and elderly Cardiovascular-Kidney-Metabolic syndrome patients by social and environmental determinants of health: an interpretable machine learning approach using longitudinal data from China.

BACKGROUND: Cardiovascular-Kidney-Metabolic (CKM) syndrome is a systemic disease characterized by pathophysiological interactions between the cardiova...

Detection of Depressive Symptoms in College Students Using Multimodal Passive Sensing Data and Light Gradient Boosting Machine: Longitudinal Pilot Study.

BACKGROUND: Depression is the top contributor to global disability. Early detection of depression and depressive symptoms enables timely intervention ...

Early detection of occupational stress: Enhancing workplace safety with machine learning and large language models.

Occupational stress is a major concern for employers and organizations as it compromises decision-making and overall safety of workers. Studies indica...

Efficacy of rTMS on SCZ symptoms: Insights from connectome mapping.

Repetitive transcranial magnetic stimulation (rTMS) is a potential treatment for schizophrenia (SCZ), yet its efficacy and underlying mechanisms remai...

Application of machine learning algorithms to identify risk factors for depression in type 2 diabetes mellitus patients: A Taiwan diabetes registry study.

BACKGROUND: We analyzed variables reported during routine clinical practice using a registrational database to estimate risk factors for depression in...

Clinical, genetic, and sociodemographic predictors of symptom severity after internet-delivered cognitive behavioural therapy for depression and anxiety.

BACKGROUND: Internet-delivered cognitive behavioural therapy (ICBT) is an effective and accessible treatment for mild to moderate depression and anxie...

Evaluating and modifying the PHDI for depression prevention: insights from NHANES 2005-2018.

BACKGROUND: Depression is a significant focus in mental health research, emerging as a pressing public health concern globally. The Planetary Health D...

TTFNet: Temporal-Frequency Features Fusion Network for Speech based Automatic Depression Recognition and Assessment.

Related studies have revealed that the phonological features of depressed patients are different from those of healthy individuals. With the increasin...

ChatGPT-4o vs Psychiatrists in Responding to Common Antidepressant Concerns.

PurposeArtificial intelligence (AI) is increasingly integrated into healthcare, including psychiatric care. This study evaluates ChatGPT-4o's reliabil...

Inflammatory biomarkers as predictors for unlocking antidepressant efficacy: Assessing predictive value and risk stratification in major depressive disorder in a prospective longitudinal study.

BACKGROUND: Major depressive disorder (MDD) is characterized by significant heterogeneity in treatment response, with inflammation hypothesized to pla...

Contrastive functional connectivity defines neurophysiology-informed symptom dimensions in major depression.

Major depressive disorder (MDD) is highly heterogeneous, posing challenges for effective treatment due to complex interactions between clinical sympto...

Brain stimulation outcome prediction in Major Depressive Disorder by deep learning models using EEG representations.

Major Depressive Disorder (MDD) is known as a widespread illness and needs a timely treatment. The treatment procedure is currently based on the trial...

Machine Learning and Artificial Intelligence in Suicide Prevention: A Bibliometric Analysis of Emerging Trends and Implications for Nursing.

Nurses play a crucial role in suicide prevention, yet the integration of artificial intelligence and machine learning technologies into nursing practi...

Predicting rTMS treatment response in depression: use of machine learning models to identify the roles of metabolic and clinical factors.

BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for depression in patients with major depressive disorder (M...

Functional connectome-based predictive modeling of suicidal ideation.

Suicide represents an egregious threat to society despite major advancements in medicine, in part due to limited knowledge of the biological mechanism...

Machine learning models of depression in middle-aged and older adults with cardiovascular metabolic diseases.

BACKGROUND: The incidence of cardiovascular metabolic diseases (CMD) is increasing, and depression in CMD patients significantly impacts prognosis. Th...

Hemoglobin - to - red cell distribution width ratio in depression symptoms: Threshold effects and metabolic - inflammatory mediation revealed by multimodal machine learning and symptom network analysis in 196,260 adults.

OBJECTIVE: To investigate the association between hemoglobin to red blood cell distribution width ratio (HRR) and depression symptoms.

The Role of Generative Artificial Intelligence in Evaluating Adherence to Responsible Press Media Reports on Suicide: A Multi-Site, Three-Language Study.