Keeping up with innovation: apredictive framework for modeling healthcare data with evolving clinical interventions

Sunil Kumar Gupta, Santu Rana, Dinh Phung, Svetha Venkatesh

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

5 Citations (Scopus)


Medical outcomes are inexorably linked to patient illness and clinical interventions. Interventions change the course of disease, crucially determining outcome. Traditional outcome prediction models build a single classifier by augmenting interventions with disease information. Interventions, however, differentially affect prognosis, thus a single prediction rule may not suffice to capture variations. Interventions also evolve over time as more advanced interventions replace older ones. To this end, we propose a Bayesian nonparametric, supervised framework that models a set of intervention groups through a mixture distribution building a separate prediction rule for each group, and allows the mixture distribution to change with time. This is achieved by using a hierarchical Dirichlet process mixture model over the interventions. The outcome is then modeled as conditional on both the latent grouping and the disease information through a Bayesian logistic regression. Experiments on synthetic and medical cohorts for 30-day readmission prediction demonstrate the superiority of the proposed model over clinical and data mining baselines.

Original languageEnglish
Title of host publicationProceedings of the 2014 SIAM International Conference on Data Mining
EditorsZoran Obradovic, Mohammed J. Zaki, Pang Ning Tan, Arindam Banerjee, Chandrika Kamath
Place of PublicationPhiladelphia PA USA
PublisherSociety for Industrial & Applied Mathematics (SIAM)
Number of pages9
ISBN (Electronic)9781510811515, 9781611973440
Publication statusPublished - 2014
Externally publishedYes
EventSIAM International Conference on Data Mining 2014 - Philadelphia, United States of America
Duration: 24 Apr 201426 Apr 2014
Conference number: 14th


ConferenceSIAM International Conference on Data Mining 2014
Abbreviated titleSDM 2014
Country/TerritoryUnited States of America
Internet address

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