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Intervention-driven predictive framework for modeling healthcare data
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posted on 2014-01-01, 00:00 authored by Santu RanaSantu Rana, Sunil GuptaSunil Gupta, Quoc-Dinh Phung, Svetha VenkateshSvetha VenkateshAssessing prognostic risk is crucial to clinical care, and critically dependent on both diagnosis and medical interventions. Current methods use this augmented information to build a single prediction rule. But this may not be expressive enough to capture differential effects of interventions on prognosis. To this end, we propose a supervised, Bayesian nonparametric framework that simultaneously discovers the latent intervention groups and builds a separate prediction rule for each intervention group. The prediction rule is learnt using diagnosis data through a Bayesian logistic regression. For inference, we develop an efficient collapsed Gibbs sampler. We demonstrate that our method outperforms baselines in predicting 30-day hospital readmission using two patient cohorts - Acute Myocardial Infarction and Pneumonia. The significance of this model is that it can be applied widely across a broad range of medical prognosis tasks. © 2014 Springer International Publishing.
History
Title of book
Advances in knowledge discovery and data mining : 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014, proceedingsVolume
8443 Part 1Issue
PART 1Series
Lecture Notes in Artificial IntelligenceChapter number
41Pagination
497 - 508Publisher
SpringerPlace of publication
Berlin, GermanyPublisher DOI
ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319066080Language
engPublication classification
B1 Book chapter; B Book chapterCopyright notice
2014, SpringerExtent
50Editor/Contributor(s)
V Tseng, T Ho, Z Zhou, A Chen, H KaoUsage metrics
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