You are not logged in.

Intervention-driven predictive framework for modeling healthcare data

Rana,S, Gupta,SK, Phung,D and Venkatesh,S 2014, Intervention-driven predictive framework for modeling healthcare data. In Tseng,VS, Ho,TB, Zhou,ZH, Chen,ALP and Kao,HY (ed), Advances in knowledge discovery and data mining : 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014, proceedings, Springer, Berlin, Germany, pp.497-508, doi: 10.1007/978-3-319-06608-0_41.

Attached Files
Name Description MIMEType Size Downloads

Title Intervention-driven predictive framework for modeling healthcare data
Author(s) Rana,SORCID iD for Rana,S orcid.org/0000-0003-2247-850X
Gupta,SKORCID iD for Gupta,SK orcid.org/0000-0002-3308-1930
Phung,DORCID iD for Phung,D orcid.org/0000-0002-9977-8247
Venkatesh,SORCID iD for Venkatesh,S orcid.org/0000-0001-8675-6631
Title of book Advances in knowledge discovery and data mining : 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014, proceedings
Editor(s) Tseng,VS
Ho,TB
Zhou,ZH
Chen,ALP
Kao,HY
Publication date 2014
Series Lecture Notes in Artificial Intelligence
Chapter number 41
Total chapters 50
Start page 497
End page 508
Total pages 12
Publisher Springer
Place of Publication Berlin, Germany
Keyword(s) Bayesian nonparametric
Healthcare data modelling
Summary Assessing 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.
ISBN 9783319066080
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-319-06608-0_41
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2014, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30072130

Document type: Book Chapter
Collection: Centre for Pattern Recognition and Data Analytics
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 1 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 233 Abstract Views, 3 File Downloads  -  Detailed Statistics
Created: Mon, 20 Apr 2015, 11:46:41 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.