Survival association rule mining towards type 2 diabetes risk assessment.

Gyorgy J. Simon, John Schrom, M. Regina Castro, Peter W. Li, Pedro J. Caraballo

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


Type-2 Diabetes Mellitus is a growing epidemic that often leads to severe complications. Effective preventive measures exist and identifying patients at high risk of diabetes is a major health-care need. The use of association rule mining (ARM) is advantageous, as it was specifically developed to identify associations between risk factors in an interpretable form. Unfortunately, traditional ARM is not directly applicable to survival outcomes and it lacks the ability to compensate for confounders and to incorporate dosage effects. In this work, we propose Survival Association Rule (SAR) Mining, which addresses these shortcomings. We demonstrate on a real diabetes data set that SARs are naturally more interpretable than the traditional association rules, and predictive models built on top of these rules are very competitive relative to state of the art survival models and substantially outperform the most widely used diabetes index, the Framingham score.

Original languageEnglish (US)
Pages (from-to)1293-1302
Number of pages10
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
StatePublished - 2013
Externally publishedYes


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