Depression

Prediction of depressive episodes based on clinical features, cognitive characteristics, inflammation-related proteins, and EEG data.

The absence of clinically validated biomarkers and objective diagnostic protocols hinders the accurate and effective diagnosis of depression. Although machine learning has been increasingly explored in psychiatric diagnosis, there remains a pressing need to develop a reliable tool that integrates multimodal data-such as clinical features, cognitive functions, electroencephalographic microstates, a...

Developing a machine learning-based predictive model for depression risk in patients with cardiovascular diseases.

BACKGROUND: Cardiovascular Diseases (CVD) are frequently comorbid with depression, significantly affecting patient prognosis and quality of life. This study aimed to develop and validate a prediction model using Machine Learning (ML) for estimating current depression risk in patients with CVD. METHODS: Data were obtained from the China Health and Retirement Longitudinal Study (CHARLS). The 2020 wa...

Machine learning models for detecting suicidal ideation in Chinese in-patients with major depressive disorder: A single-centre retrospective study.

UNLABELLED: Suicide claims >720,000 lives annually; major depressive disorder (MDD) carries the highest population-attributable risk. Suicidal ideatio...

Predicting suicidal ideation from depression screening data: A network-augmented machine learning approach.

BACKGROUND: Suicidal ideation is often assessed using a single self-report item in routine screening. We developed a model that combines machine learn...

Altered brain network topology in adolescents with major depressive disorder and bipolar disorder: A resting-state fMRI graph-theoretical and machine learning study.

BACKGROUND: Adolescents with major depressive disorder (MDD) and bipolar disorder (BD) share substantial clinical overlap and elevated suicide risk, y...

Multi-domain ECG feature extraction enable accurate automated recognition of bipolar disorder, depression and schizophrenia.

OBJECTIVE: To develop and validate a multi-lead electrocardiogram (ECG)-based machine learning system for automated classification of major psychiatri...

Deep learning using electroencephalogram (EEG) data for diagnosing and predicting SSRI response in major depressive disorder.

BACKGROUND: Major Depression (MDD) is a potentially life-threatening condition that ranks among the diseases with the highest global burden. Despite i...

Differentiating Depression Subtypes through three-electrode EEG and Emotional Stimuli: A Machine Learning Approach.

We propose an EEG-based framework for depression subtype assessment using emotion-modulated neural dynamics elicited by immersive virtual reality (VR)...

Identifying postpartum depression subtypes using natural language processing and clinical notes.

BACKGROUND: Postpartum depression (PPD) remains vastly underdiagnosed, and its clinical heterogeneity is not well understood. Diagnosis codes in elect...

Attitudes toward artificial intelligence among individuals with anxiety, depression, bipolar disorders, and schizophrenia.

BACKGROUND: Artificial intelligence (AI) increasingly supports medical diagnosis, interventions, and clinical decision-making. In various domains of h...

A Therapeutic Conversational Agent (Solace) for Management of Chronic Pain: Acceptability and Usability Study.

BACKGROUND: Chronic pain is a critical cause of personal suffering and societal concern. However, treatment options remain inadequate, and access to e...

A proof-of-concept machine learning model for short-term suicide risk stratification in depressed youth.

Machine learning (ML) offers promise for suicide risk stratification in depressed youth, yet its clinical application remains methodologically challen...

The use of smart surveillance technologies for suicide prevention in public spaces: a professional stakeholder survey from the United Kingdom.

Utilization and Feasibility of a Wearable Device in Patients With Sedative Effects of Drugs: Protocol for a Prospective Observational Study for the Advanced Respiratory Monitoring Events in Drug Toxicity (ARM-ED) Study.

BACKGROUND: Drug-related deaths worldwide are most commonly attributed to opioids. Opioids and other sedative drugs can cause respiratory depression a...

Distinct Biotypes of Visual Perception in Major Depressive Disorder.

Previous studies showed abnormalities in both visual motion perception (VMP) and occipital cortex activity in subjects suffering from major depressive...

Risk-prediction models for postpartum depression: Assessment of risk of bias using PROBAST+AI.

OBJECTIVE: The postpartum depression (PPD) risk prediction model is an effective risk stratification tool and is expected to play a significant role i...

Life events extraction from healthcare notes for veteran acute suicide risk prediction.

OBJECTIVE: Predictive models of suicide risk have focused on features extracted from structured data found in electronic health records, with limited ...

Utilizing stimulation-evoked hemodynamic activity to predict antidepressant response to intermittent theta-burst stimulation in adults with major depression.

BACKGROUND: The instantaneous neural response to prefrontal theta burst stimulation (TBS) may serve as predictive marker for antidepressant treatment ...

An artificial intelligence approach to support adolescent suicide prevention initiatives in the United States.

Adolescent suicide remains a critical public health issue in the United States, with complex, interrelated risk and protective factors operating acros...

Machine learning-based methods for predicting postpartum depression: A review.

Postpartum depression (PPD) is a widespread mental illness after delivery, which has a substantial impact on the health of both mothers and infants. M...