AIMC Topic: Depression

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Machine learning discovery of longitudinal patterns of depression and suicidal ideation.

PloS one
BACKGROUND AND AIM: Depression is often accompanied by thoughts of self-harm, which are a strong predictor of subsequent suicide attempt and suicide death. Few empirical data are available regarding the temporal correlation between depression symptom...

Machine Learning to Understand the Immune-Inflammatory Pathways in Fibromyalgia.

International journal of molecular sciences
Fibromyalgia (FM) is a chronic syndrome characterized by widespread musculoskeletal pain, and physical and emotional symptoms. Although its pathophysiology is largely unknown, immune-inflammatory pathways may be involved. We examined serum interleuki...

Machine learning and big data: Implications for disease modeling and therapeutic discovery in psychiatry.

Artificial intelligence in medicine
INTRODUCTION: Machine learning capability holds promise to inform disease models, the discovery and development of novel disease modifying therapeutics and prevention strategies in psychiatry. Herein, we provide an introduction on how machine learnin...

Identification of Patients in Need of Advanced Care for Depression Using Data Extracted From a Statewide Health Information Exchange: A Machine Learning Approach.

Journal of medical Internet research
BACKGROUND: As the most commonly occurring form of mental illness worldwide, depression poses significant health and economic burdens to both the individual and community. Different types of depression pose different levels of risk. Individuals who s...

Depression recognition using machine learning methods with different feature generation strategies.

Artificial intelligence in medicine
The diagnosis of depression almost exclusively depends on doctor-patient communication and scale analysis, which have the obvious disadvantages such as patient denial, poor sensitivity, subjective biases and inaccuracy. An objective, automated method...

Identifying depression in the National Health and Nutrition Examination Survey data using a deep learning algorithm.

Journal of affective disorders
BACKGROUND: As depression is the leading cause of disability worldwide, large-scale surveys have been conducted to establish the occurrence and risk factors of depression. However, accurately estimating epidemiological factors leading up to depressio...

The Current Research Landscape on the Artificial Intelligence Application in the Management of Depressive Disorders: A Bibliometric Analysis.

International journal of environmental research and public health
Artificial intelligence (AI)-based techniques have been widely applied in depression research and treatment. Nonetheless, there is currently no systematic review or bibliometric analysis in the medical literature about the applications of AI in depre...

Early Detection of Depression: Social Network Analysis and Random Forest Techniques.

Journal of medical Internet research
BACKGROUND: Major depressive disorder (MDD) or depression is among the most prevalent psychiatric disorders, affecting more than 300 million people globally. Early detection is critical for rapid intervention, which can potentially reduce the escalat...

Using heart rate profiles during sleep as a biomarker of depression.

BMC psychiatry
BACKGROUND: Abnormalities in heart rate during sleep linked to impaired neuro-cardiac modulation may provide new information about physiological sleep signatures of depression. This study assessed the validity of an algorithm using patterns of heart ...

Artificial Intelligence based facial recognition for Mood Charting among men on life style modification and it's correlation with cortisol.

Asian journal of psychiatry
UNLABELLED: Today, clinicians and researchers believe that mood disorders in children and adolescents remain one of the most under diagnosed mental health problems. Mood disorders in adolescents also put them at risk for other conditions that may per...