Comparison of bio-inspired algorithms applied to the hospital mortality risk stratification
The 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’ severi...
- Autores:
-
Silva, Jesús
Herazo-Beltrán, Yaneth
Marín-González, Freddy
Varela Izquierdo, Noel
Pineda, Omar
Palencia-Domínguez, Pablo
Vargas Mercado, Carlos
Marín González, Freddy
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7741
- Acceso en línea:
- https://hdl.handle.net/11323/7741
https://doi.org/10.1007/978-981-15-4875-8_16
https://repositorio.cuc.edu.co/
- Palabra clave:
- Hospital mortality
Risk stratification
Intensive care unit
Artificial neural networks
Bootstrap
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Comparison of bio-inspired algorithms applied to the hospital mortality risk stratification |
| title |
Comparison of bio-inspired algorithms applied to the hospital mortality risk stratification |
| spellingShingle |
Comparison of bio-inspired algorithms applied to the hospital mortality risk stratification Hospital mortality Risk stratification Intensive care unit Artificial neural networks Bootstrap |
| title_short |
Comparison of bio-inspired algorithms applied to the hospital mortality risk stratification |
| title_full |
Comparison of bio-inspired algorithms applied to the hospital mortality risk stratification |
| title_fullStr |
Comparison of bio-inspired algorithms applied to the hospital mortality risk stratification |
| title_full_unstemmed |
Comparison of bio-inspired algorithms applied to the hospital mortality risk stratification |
| title_sort |
Comparison of bio-inspired algorithms applied to the hospital mortality risk stratification |
| dc.creator.fl_str_mv |
Silva, Jesús Herazo-Beltrán, Yaneth Marín-González, Freddy Varela Izquierdo, Noel Pineda, Omar Palencia-Domínguez, Pablo Vargas Mercado, Carlos Marín González, Freddy |
| dc.contributor.author.spa.fl_str_mv |
Silva, Jesús Herazo-Beltrán, Yaneth Marín-González, Freddy Varela Izquierdo, Noel Pineda, Omar Palencia-Domínguez, Pablo Vargas Mercado, Carlos |
| dc.contributor.author.none.fl_str_mv |
Marín González, Freddy |
| dc.subject.spa.fl_str_mv |
Hospital mortality Risk stratification Intensive care unit Artificial neural networks Bootstrap |
| topic |
Hospital mortality Risk stratification Intensive care unit Artificial neural networks Bootstrap |
| description |
The 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 known. |
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2020 |
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2020 |
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2021-01-21T13:39:17Z |
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2021-01-21T13:39:17Z |
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https://doi.org/10.1007/978-981-15-4875-8_16 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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eng |
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eng |
| dc.relation.references.spa.fl_str_mv |
1. Sargent, D.J.: Comparison of artificial neural networks with other statistical approaches results from medical data sets. Cancer 91, 1636–1642 (2001) 2. Bifet, A., De Morales, G. F: Big data stream learning with Samoa. Recuperado de (2014). 3. 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) 4. 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) 5. Bravo, M., Alvarado, M.: Similarity measures for substituting web services. Int. J. Web Serv. Res. 7(3), 1–29 (2010) 6. 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) 7. 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) 8. 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) 9. Zhu, J., Fang, X. et al.: IBM cloud computing powering a smarter planet. In: Libro Cloud Computing, vol. 599.51, pp 621– 625 (2009) 10. Mohanty, R., Ravi, V., Patra, M.R.: Web-services classification using intelligent techniques. Expert Syst. Appl. 37(7), 5484–5490 (2010) 11. Thames, L., Schaefer, D.: Software defined cloud manufacturing for industry 4.0. Procedía CIRP 52, 12–17 (2016) 12. 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) 13. 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) 14. 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) 15. Viloria, A., Neira-Rodado, D., Pineda Lezama, O. B.: Recovery of scientific data using intelligent distributed data warehouse. ANT/EDI40, pp 1249–1254 (2019) 16. 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) |
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Silva, JesúsHerazo-Beltrán, YanethMarín-González, FreddyVarela Izquierdo, NoelPineda, OmarPalencia-Domínguez, PabloVargas Mercado, CarlosMarín González, Freddyvirtual::358-12021-01-21T13:39:17Z2021-01-21T13:39:17Z2020https://hdl.handle.net/11323/7741https://doi.org/10.1007/978-981-15-4875-8_16Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The 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 known.Silva, JesúsHerazo-Beltrán, YanethMarín González, Freddy-will be generated-orcid-0000-0002-3935-8806-600Varela Izquierdo, Noel-will be generated-orcid-0000-0001-7036-4414-600Pineda, Omar-will be generated-orcid-0000-0002-8239-3906-600Palencia-Domínguez, Pablo-will be generated-orcid-0000-0003-3679-6015-600Vargas Mercado, Carlos-will be generated-orcid-0000-0002-5436-0568-600application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Smart Innovation, Systems and Technologieshttps://link.springer.com/chapter/10.1007/978-981-15-4875-8_16Hospital mortalityRisk stratificationIntensive care unitArtificial neural networksBootstrapComparison of bio-inspired algorithms applied to the hospital mortality risk stratificationArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Sargent, D.J.: Comparison of artificial neural networks with other statistical approaches results from medical data sets. Cancer 91, 1636–1642 (2001)2. Bifet, A., De Morales, G. F: Big data stream learning with Samoa. Recuperado de (2014).3. 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)4. 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)5. Bravo, M., Alvarado, M.: Similarity measures for substituting web services. Int. J. Web Serv. Res. 7(3), 1–29 (2010)6. 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)7. 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)8. 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)9. Zhu, J., Fang, X. et al.: IBM cloud computing powering a smarter planet. In: Libro Cloud Computing, vol. 599.51, pp 621– 625 (2009)10. Mohanty, R., Ravi, V., Patra, M.R.: Web-services classification using intelligent techniques. Expert Syst. Appl. 37(7), 5484–5490 (2010)11. Thames, L., Schaefer, D.: Software defined cloud manufacturing for industry 4.0. Procedía CIRP 52, 12–17 (2016)12. 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)13. 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)14. 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)15. Viloria, A., Neira-Rodado, D., Pineda Lezama, O. B.: Recovery of scientific data using intelligent distributed data warehouse. ANT/EDI40, pp 1249–1254 (2019)16. Wu, Q., Yan, H. S., Yang, H. B.: A forecasting model based support vector machine and particle swarm optimization. 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