A predictive framework for modeling healthcare data with evolving clinical interventions

Rana, Santu, Gupta, Sunil, Phung, Dinh and Venkatesh, Svetha 2015, A predictive framework for modeling healthcare data with evolving clinical interventions, Statistical analysis and data mining, vol. 8, no. 3, pp. 162-182, doi: 10.1002/sam.11262.

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Title A predictive framework for modeling healthcare data with evolving clinical interventions
Author(s) Rana, SantuORCID iD for Rana, Santu orcid.org/0000-0003-2247-850X
Gupta, SunilORCID iD for Gupta, Sunil orcid.org/0000-0002-3308-1930
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Journal name Statistical analysis and data mining
Volume number 8
Issue number 3
Start page 162
End page 182
Total pages 21
Publisher Wiley
Place of publication London, Eng.
Publication date 2015-01
ISSN 1932-1864
Summary Medical interventions critically determine clinical outcomes. But prediction models either ignore interventions or dilute impact by building a single prediction rule by amalgamating interventions with other features. One rule across all interventions may not capture differential effects. Also, interventions change with time as innovations are made, requiring prediction models to evolve over time. To address these gaps, we propose a prediction framework that explicitly models interventions by extracting a set of latent intervention groups through a Hierarchical Dirichlet Process (HDP) mixture. Data are split in temporal windows and for each window, a separate distribution over the intervention groups is learnt. This ensures that the model evolves with changing interventions. The outcome is modeled as conditional, on both the latent grouping and the patients' condition, through a Bayesian logistic regression. Learning distributions for each time-window result in an over-complex model when interventions do not change in every time-window. We show that by replacing HDP with a dynamic HDP prior, a more compact set of distributions can be learnt. Experiments performed on two hospital datasets demonstrate the superiority of our framework over many existing clinical and traditional prediction frameworks.
Language eng
DOI 10.1002/sam.11262
Indigenous content off
Field of Research 080109 Pattern Recognition and Data Mining
0104 Statistics
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Wiley
Persistent URL http://hdl.handle.net/10536/DRO/DU:30077466

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