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dc.creatorSilva, Jesús
dc.creatorHerazo-Beltrán, Yaneth
dc.creatorMarín González, Freddy
dc.creatorVarela Izquierdo, Noel
dc.creatorPineda, Omar
dc.creatorPalencia-Domínguez, Pablo
dc.creatorVargas Mercado, Carlos
dc.description.abstractThe construction of patient classification (or risk adjustment) systems allows comparison of the effectiveness and quality of hospitals and hospital services, providing useful information for management decision making and management of hospitals. Risk adjustment systems to stratify patients’ severity in a clinical outcome are generally constructed from care variables and using statistical techniques based on logistic regression (RL). The objective of this investigation is to compare the hospital mortality prediction capacity of an artificial neural network (RNA) with other methods already
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.sourceSmart Innovation, Systems and Technologiesspa
dc.subjectHospital mortalityspa
dc.subjectRisk stratificationspa
dc.subjectIntensive care unitspa
dc.subjectArtificial neural networksspa
dc.titleComparison of bio-inspired algorithms applied to the hospital mortality risk stratificationspa
dcterms.references1. Sargent, D.J.: Comparison of artificial neural networks with other statistical approaches results from medical data sets. Cancer 91, 1636–1642 (2001)spa
dcterms.references2. Bifet, A., De Morales, G. F: Big data stream learning with Samoa. Recuperado de (2014).spa
dcterms.references3. Clermont, G., Angus, D.C., DiRusso, S.M., Griffin, M., Linde-Zwirble, W.T.: Predicting hospital mortality for patients in the intensive care unit: A comparison of artificial neural networks with logistic regression models. Crit. Care Med. 29, 291–296 (2001)spa
dcterms.references4. Wong, L.S.S., Young, J.D.: A comparison of ICU mortality prediction using the APACHE II scoring system and artificial neural network. Anaesthesia 54, 1048–1054 (1999)spa
dcterms.references5. Bravo, M., Alvarado, M.: Similarity measures for substituting web services. Int. J. Web Serv. Res. 7(3), 1–29 (2010)spa
dcterms.references6. Chen, L., Zhang, Y., Song, Z.L., Miao, Z.: Automatic web services classification based on rough set theory. J. Cental South Univ. 20, 2708–2714 (2013)spa
dcterms.references7. Viloria, A., Lezama, O. B. P: Improvements for determining the number of clusters in k-means for innovation databases in SMEs. ANT/EDI40, pp 1201–1206 (2019)spa
dcterms.references8. Viloria, A., Lis-Gutiérrez J. P., Gaitán-Angulo, M., Godoy, A. R. M., Moreno, G. C., Kamatkar, S. J.: Methodology for the design of a student pattern recognition tool to facilitate the teaching—Learning process through knowledge data discovery (big data). In: Tan, Y., Shi, Y., Tang, Q. (eds.) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham (2018)spa
dcterms.references9. Zhu, J., Fang, X. et al.: IBM cloud computing powering a smarter planet. In: Libro Cloud Computing, vol. 599.51, pp 621– 625 (2009)spa
dcterms.references10. Mohanty, R., Ravi, V., Patra, M.R.: Web-services classification using intelligent techniques. Expert Syst. Appl. 37(7), 5484–5490 (2010)spa
dcterms.references11. Thames, L., Schaefer, D.: Software defined cloud manufacturing for industry 4.0. Procedía CIRP 52, 12–17 (2016)spa
dcterms.references12. Álvarez, M., Nava, J.M., Rue, M., Quintana, S.: Mortality prediction in head trauma patients: Performance of glasgow coma score and general severity systems. Crit. Care Med. 26, 142–148 (1998)spa
dcterms.references13. Setnes, M., Kaymak, U.: Fuzzy modeling of client preference from large data sets: an application to target selection in direct marketing. IEEE Trans. Fuzzy Syst. 9(1), 153–163 (2001)spa
dcterms.references14. Llorca, J., Dierssen, T.: Comparación de dos métodos para el cálculo de la incertidumbre en los análisis de laboratorio. Gac. Sanit. 14, 458–463 (2000)spa
dcterms.references15. Viloria, A., Neira-Rodado, D., Pineda Lezama, O. B.: Recovery of scientific data using intelligent distributed data warehouse. ANT/EDI40, pp 1249–1254 (2019)spa
dcterms.references16. Wu, Q., Yan, H. S., Yang, H. B.: A forecasting model based support vector machine and particle swarm optimization. In: 2008 Workshop on Power Electronics and Intelligent Transportation System, pp. 218–222 (2008)spa

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