Intervention-driven predictive framework for modeling healthcare data

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

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

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication18th Pacific-Asia Conference, PAKDD 2014 Tainan, Taiwan, May 13-16, 2014 Proceedings, Part I
EditorsVincent S. Tseng, Tu Bao Ho, Zhi-Hua Zhou, Arbee L.P. Chen, Hung-Yu Kao
Place of PublicationCham Switzerland
Number of pages12
ISBN (Electronic)9783319066080
ISBN (Print)9783319066073
Publication statusPublished - 2014
Externally publishedYes
EventPacific-Asia Conference on Knowledge Discovery and Data Mining 2014 - Tainan, Taiwan
Duration: 13 May 201416 May 2014
Conference number: 18th (Proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferencePacific-Asia Conference on Knowledge Discovery and Data Mining 2014
Abbreviated titlePAKDD 2014
Internet address


  • Bayesian nonparametric
  • Healthcare data modelling

Cite this