Machine learning-driven risk prediction and feature identification for major depressive disorder and its progression: an exploratory study based on five years of longitudinal data from the US national health survey.

Journal: Journal of affective disorders

PMID: 40221055

Abstract

BACKGROUND: Major depressive disorder (MDD) presents significant public health challenges due to its increasing prevalence and complex risk factors. This study systematically analyzed data from 2019 to 2023 to explore trends in MDD incidence, symptom progression, and multidimensional influencing factors.

Authors

Youbei Lin

Jinzhou Medical University, School of Nursing, Jinzhou City, Liaoning Province, 121001, China.
Chuang Li

Center for Bio-inspired Energy Science, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA.
Hongyu Li

Electrical Engineering, University at Buffalo, State University of New York, Buffalo, NY, United States.

Keywords

Adult Depressive Disorder, Major Disease Progression Female Health Surveys Humans Incidence Longitudinal Studies Machine Learning Male Middle Aged Risk Assessment Risk Factors Social Support United States

External Resources

View on PubMed Access via DOI PubMed (40221055)

Machine learning-driven risk prediction and feature identification for major depressive disorder and its progression: an exploratory study based on five years of longitudinal data from the US national health survey.

Abstract

Authors

Keywords

External Resources

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