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Improving risk prediction for depression via Elastic Net regression - Results from Korea National Health Insurance Services Data.

TitleImproving risk prediction for depression via Elastic Net regression - Results from Korea National Health Insurance Services Data.
Publication TypeJournal Article
Year of Publication2016
AuthorsKim M-H, Banerjee S, Park SMin, Pathak J
JournalAMIA Annu Symp Proc
Volume2016
Pagination1860-1869
Date Published2016
ISSN1942-597X
KeywordsAdult, Area Under Curve, Comorbidity, Depression, Female, Humans, Logistic Models, Male, Middle Aged, National Health Programs, Prevalence, Republic of Korea, Risk, ROC Curve
Abstract

Depression, despite its high prevalence, remains severely under-diagnosed across the healthcare system. This demands the development of data-driven approaches that can help screen patients who are at a high risk of depression. In this work, we develop depression risk prediction models that incorporate disease co-morbidities using logistic regression with Elastic Net. Using data from the one million twelve-year longitudinal cohort from Korean National Health Insurance Services (KNHIS), our model achieved an Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) of 0.7818, compared to a traditional logistic regression model without co-morbidity analysis (AUC of 0.6992). We also showed co-morbidity adjusted Odds Ratios (ORs), which may be more accurate independent estimate of each predictor variable. In conclusion, inclusion of co-morbidity analysis improved the performance of depression risk prediction models.

Alternate JournalAMIA Annu Symp Proc
PubMed ID28269945
PubMed Central IDPMC5333336
Grant ListR01 GM105688 / GM / NIGMS NIH HHS / United States
R01 MH105384 / MH / NIMH NIH HHS / United States
UL1 TR000457 / TR / NCATS NIH HHS / United States
UL1 TR002384 / TR / NCATS NIH HHS / United States
Category: 
Faculty Publication Student Publication