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Logic Synthesis as an Efficient Means of Minimal Model Discovery from Multivariable Medical Datasets

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dc.contributor.author Gorji, Niku
dc.contributor.author Poon, Simon
dc.date.accessioned 2017-12-28T01:43:40Z
dc.date.available 2017-12-28T01:43:40Z
dc.date.issued 2018-01-03
dc.identifier.isbn 978-0-9981331-1-9
dc.identifier.uri http://hdl.handle.net/10125/50242
dc.description.abstract In this paper we review the application of logic synthesis methods for uncovering minimal structures in observational/medical datasets. Traditionally used in digital circuit design, logic synthesis has taken major strides in the past few decades and forms the foundation of some of the most powerful concepts in computer science and data mining. Here we provide a review of current state of research in application of logic synthesis methods for data analysis and provide a demonstrative example for systematic application and reasoning based on these methods.
dc.format.extent 10 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 51st Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Big Data on Healthcare Application
dc.subject logic synthesis medical data analysis minimal model discovery
dc.title Logic Synthesis as an Efficient Means of Minimal Model Discovery from Multivariable Medical Datasets
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2018.354
Appears in Collections: Big-Data on Healthcare Application


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