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A framework for mixed-type multioutcome prediction with applications in healthcare
journal contribution
posted on 2017-07-01, 00:00 authored by Budhaditya Saha, Sunil GuptaSunil Gupta, Quoc-Dinh Phung, Svetha VenkateshSvetha VenkateshHealth analysis often involves prediction of multiple outcomes of mixed type. The existing work is restrictive to either a limited number or specific outcome types. We propose a framework for mixed-type multioutcome 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 (nonnegative outcome). To model 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. 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 (nonnegative), and emergency time to next admission day (nonnegative). We show that the predictive performance of the proposed model is better than several state-of-the-art baselines.
History
Journal
IEEE journal of biomedical and health informaticsVolume
21Issue
4Pagination
1182 - 1191Publisher
IEEELocation
Piscataway, N.J.Publisher DOI
eISSN
2168-2208Language
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2017, IEEEUsage metrics
Keywords
health information managementmulti-task learningoptimizationScience & TechnologyTechnologyLife Sciences & BiomedicineComputer Science, Information SystemsComputer Science, Interdisciplinary ApplicationsMathematical & Computational BiologyMedical InformaticsComputer ScienceREGRESSIONArtificial Intelligence and Image Processing
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