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. Attached Files Name Description MIMEType Size Downloads Title A framework for mixed-type multi-outcome prediction with applications in healthcare Author(s) Saha, Budhaditya Gupta, Sunil orcid.org/0000-0002-3308-1930 Phung, Quoc-Dinh orcid.org/0000-0002-9977-8247 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 Document type: Journal Article Collections: School of Information Technology Centre for Pattern Recognition and Data Analytics Related Links Link Description Connect to Elements publication management system Go to link with your DU access privileges   Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 04 Jul 2016, 11:19:22 EST Mon, 04 Jul 2016, 13:48:58 EST Mon, 04 Jul 2016, 14:56:24 EST Wed, 24 Aug 2016, 12:17:40 EST Fri, 26 Aug 2016, 06:08:42 EST Wed, 12 Oct 2016, 08:18:14 EST Wed, 12 Oct 2016, 08:21:45 EST Wed, 12 Oct 2016, 08:22:34 EST Fri, 19 May 2017, 11:14:55 EST Tue, 20 Jun 2017, 14:25:26 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 26 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Wed, 24 Aug 2016, 12:17:39 EST

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