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

Gupta, Sunil Kumar, Rana, Santu, Phung, Dinh and Venkatesh, Svetha 2014, Keeping up with innovation: a predictive framework for modeling healthcare data with evolving clinical interventions, in SDM 2014: Proceedings of the 14th SIAM International Conference on Data Mining 2014, Society for Industrial and Applied Mathematics, [Philadelphia, Pa], pp. 235-243, doi: 10.1137/1.9781611973440.27.

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Title Keeping up with innovation: a predictive framework for modeling healthcare data with evolving clinical interventions
Author(s) Gupta, Sunil KumarORCID iD for Gupta, Sunil Kumar orcid.org/0000-0002-3308-1930
Rana, SantuORCID iD for Rana, Santu orcid.org/0000-0003-2247-850X
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name International Conference on Data Mining (14th : 2014 : Philadelphia, Pennsylvania)
Conference location Philadelphia, Pennsylvania
Conference dates 24-26 Apr. 2014
Title of proceedings SDM 2014: Proceedings of the 14th SIAM International Conference on Data Mining 2014
Editor(s) Zaki,M
Obradovic,Z
Tan,PN
Banerjee,A
Kamath,C
Parthasarathy,S
Publication date 2014
Start page 235
End page 243
Total pages 9
Publisher Society for Industrial and Applied Mathematics
Place of publication [Philadelphia, Pa]
Summary 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.
ISBN 9781611973440
Language eng
DOI 10.1137/1.9781611973440.27
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, Society for Industrial and Applied Mathematics
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082827

Document type: Conference Paper
Collection: Centre for Pattern Recognition and Data Analytics
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