Hybrid Mortality Prediction Using Multiple Source Systems
| dc.contributor.author | Mativo, Isaac | |
| dc.contributor.author | Yesha, Yelena | |
| dc.contributor.author | Grasso, Michael | |
| dc.contributor.author | Oates, Tim | |
| dc.contributor.author | Zhu, Qian | |
| dc.date.accessioned | 2019-04-19T18:36:17Z | |
| dc.date.available | 2019-04-19T18:36:17Z | |
| dc.description.abstract | The 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.uri | http://airccse.org/journal/ijci/Current2019.html | en_US |
| dc.format.extent | 8 pages | en_US |
| dc.genre | journal articles | en_US |
| dc.identifier | doi:10.13016/m2vdbm-b4mg | |
| dc.identifier.citation | Isaac 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.2019.8101 | en_US |
| dc.identifier.uri | https://doi.org/10.5121/ijci.2019.8101 | |
| dc.identifier.uri | http://hdl.handle.net/11603/13474 | |
| dc.language.iso | en_US | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Information Systems Department | |
| dc.relation.ispartof | The Shriver Center at UMBC | |
| dc.rights | 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. | |
| dc.rights | Attribution 3.0 United States | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/3.0/us/ | * |
| dc.subject | decision support systems | en_US |
| dc.subject | artificial intelligence | en_US |
| dc.subject | hybrid systems | en_US |
| dc.title | Hybrid Mortality Prediction Using Multiple Source Systems | en_US |
| dc.type | Text | en_US |