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

Min Hyung Kim, Samprit Banerjee, Sang Min Park, Jyotishman Pathak

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1860-1869
Number of pages10
JournalAMIA ... Annual Symposium proceedings. AMIA Symposium
Volume2016
StatePublished - 2016

Keywords

  • Chronic Conditions Data Warehouse (CCW) Condition Algorithms
  • Co-morbidity
  • Depression
  • Elastic Net
  • Korea National Health Insurance Services Longitudinal Cohort Data
  • Least Absolute Shrinkage And Selection Operator (LASSO)
  • Logistic Regression
  • Risk Prediction Model

ASJC Scopus subject areas

  • Medicine(all)

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