|Title||Improving risk prediction for depression via Elastic Net regression - Results from Korea National Health Insurance Services Data.|
|Publication Type||Journal Article|
|Year of Publication||2016|
|Authors||Kim M-H, Banerjee S, Park SMin, Pathak J|
|Journal||AMIA Annu Symp Proc|
|Keywords||Adult, Area Under Curve, Comorbidity, Depression, Female, Humans, Logistic Models, Male, Middle Aged, National Health Programs, Prevalence, Republic of Korea, Risk, ROC Curve|
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 Journal||AMIA Annu Symp Proc|
|PubMed Central ID||PMC5333336|
|Grant List||R01 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
Improving risk prediction for depression via Elastic Net regression - Results from Korea National Health Insurance Services Data.
Submitted by dpo2001 on May 16, 2019 - 10:17am
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