AIMC Topic: Depression

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Predicting depression using serum perfluoroalkyl and polyfluoroalkyl substances levels via interpretable machine learning.

Journal of affective disorders
BACKGROUND: Per- and polyfluoroalkyl substances (PFAS) are synthetic chemicals with widespread environmental persistence and human exposure. Currently, no studies have used machine learning (ML) to predict depression based on PFAS exposure. This stud...

Development and interpretation of a machine learning risk prediction model for post-stroke depression in a Chinese population.

Scientific reports
Current evidence for predictive models of post-stroke depression (PSD) risk based on machine learning (ML) remains limited. The aim of this study is to develop a superior predictive model based on ML algorithms for PSD in the Chinese population. We r...

Identification and validation of an explainable machine learning model for vascular depression diagnosis in the older adults: a multicenter cohort study.

BMC medicine
BACKGROUND: Vascular depression (VaDep) is a prevalent affective disorder in older adults that significantly impacts functional status and quality of life. Early identification and intervention are crucial but largely insufficient in clinical practic...

Data Collection for Automatic Depression Identification in Spanish Speakers Using Deep Learning Algorithms: Protocol for a Case-Control Study.

JMIR research protocols
BACKGROUND: Depression is a mental health condition that affects millions of people worldwide. Although common, it remains difficult to diagnose due to its heterogeneous symptomatology. Mental health questionnaires are currently the most used assessm...

Optimizing treatment for depression in primary care using psychotherapy versus antidepressant medication in a low-resource setting: protocol for the OptimizeD randomized controlled trial.

BMC psychiatry
BACKGROUND: Psychotherapy and antidepressant medications are first-line treatments for depression, and they both have significant treatment effects on average. However, treatment response varies widely across patients, and neither approach is univers...

AI-based prediction of depression symptomatology in first-episode psychosis patients: insights from the EUFEST and RAISE-ETP clinical trials.

Psychological medicine
BACKGROUND: Depressive symptoms are highly prevalent in first-episode psychosis (FEP) and worsen clinical outcomes. It is currently difficult to determine which patients will have persistent depressive symptoms based on a clinical assessment. We aime...

Hysterectomy as a predictor of depression: A comprehensive analysis using logistic regression and machine learning.

Journal of affective disorders
BACKGROUND: An increasing number of studies have shown that there is an inseparable connection between hysterectomy and occurrence of depression, and the impact on patient's mental health cannot be ignored. Therefore, this study utilized the National...

Predicting depressive symptoms through social support: a machine learning approach in military populations.

European journal of psychotraumatology
Perceived Social support has been consistently shown to reduce depressive symptoms among military personnel. However, limited research has explored how different types of support, emotional, informational, and instrumental, from multiple sources uni...

Generative AI-Powered Mental Wellness Chatbot for College Student Mental Wellness: Open Trial.

JMIR formative research
BACKGROUND: Colleges have turned to digital mental health interventions to meet the increasing mental health treatment needs of their students. Among these, chatbots stand out as artificial intelligence-driven tools capable of engaging in human-like ...