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A framework for mixed-type multi-outcome prediction with applications in healthcare

Saha, Budhaditya, Gupta, Sunil, Phung, Quoc-Dinh and Venkatesh, Svetha 2016, A framework for mixed-type multi-outcome prediction with applications in healthcare, IEEE journal of biomedical and health informatics, pp. 1-11.

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Title A framework for mixed-type multi-outcome prediction with applications in healthcare
Author(s) Saha, Budhaditya
Gupta, Sunil
Phung, Quoc-Dinh
Venkatesh, Svetha
Journal name IEEE journal of biomedical and health informatics
Start page 1
End page 11
Total pages 11
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2016
ISSN 2168-2208
Summary Health analysis often involves prediction of multiple outcomes of mixed-type. Existing work is restrictive to either a limited number or specific outcome types. We propose a framework for mixed-type multi-outcome prediction. Our proposed framework proposes a cumulative loss function composed of a specific loss function for each outcome type - as an example, least square (continuous outcome), hinge (binary outcome), poisson (count outcome) and exponential (non-negative outcome). Tomodel these outcomes jointly, we impose a commonality across the prediction parameters through a common matrix-Normal prior. The framework is formulated as iterative optimization problems and solved using an efficient Block coordinate descent method (BCD). We empirically demonstrate both scalability and convergence. We apply the proposed model to a synthetic dataset and then on two real-world cohorts: a Cancer cohort and an Acute Myocardial Infarction cohort collected over a two year period. We predict multiple emergency related outcomes - as example, future emergency presentations (binary), emergency admissions (count), emergency length-of-stay-days (non-negative) and emergency time-to-next-admission-day (non-negative). Weshow that the predictive performance of the proposed model is better than several state-of-the-art baselines.
Notes In Press
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
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 ©2016, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30084635

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