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dc.contributor.authorMativo, Isaac
dc.contributor.authorYesha, Yelena
dc.contributor.authorGrasso, Michael
dc.contributor.authorOates, Tim
dc.contributor.authorZhu, Qian
dc.date.accessioned2019-04-19T18:36:17Z
dc.date.available2019-04-19T18:36:17Z
dc.description.abstractThe use of artificial intelligence in clinical care to improve decision support systems is increasing. This is not surprising since by its very nature, the practice of medicine consists of making decisions based on observations from different systems both inside and outside the human body. In this paper, we combine three general systems (ICU, diabetes, and comorbidities) and use them to make patient clinical predictions. We use an artificial intelligence approach to show that we can improve mortality prediction of hospitalized diabetic patients. We do this by utilizing a machine learning approach to select clinical input features that are more likely to predict mortality. We then use these features to create a hybrid mortality prediction model and compare our results to non artificial intelligence models. For simplicity, we limit our input features to patient comorbidities and features derived from a well-known mortality measure, the Sequential Organ Failure Assessment (SOFA).en_US
dc.description.urihttp://airccse.org/journal/ijci/Current2019.htmlen_US
dc.format.extent8 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2vdbm-b4mg
dc.identifier.citationIsaac Mativo, Yelena Yesha, et.al, Hybrid Mortality Prediction Using Multiple Source Systems , International Journal on Cybernetics & Informatics (IJCI) Vol. 8, No.1, February 2019, DOI: 10.5121/ijci.2019.8101en_US
dc.identifier.urihttps://doi.org/10.5121/ijci.2019.8101
dc.identifier.urihttp://hdl.handle.net/11603/13474
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Information Systems Department
dc.relation.ispartofThe Shriver Center at UMBC
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.subjectdecision support systemsen_US
dc.subjectartificial intelligenceen_US
dc.subjecthybrid systemsen_US
dc.titleHybrid Mortality Prediction Using Multiple Source Systemsen_US
dc.typeTexten_US


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This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
Except where otherwise noted, this item's license is described as This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.