Using multivariate sequential patterns to improve survival prediction in intensive care burn unit
| Data(s) |
23/12/2016
23/12/2016
2015
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|---|---|
| Resumo |
Resuscitation and stabilization are key issues in Intensive Care Burn Units and early survival predictions help to decide the best clinical action during these phases. Current survival scores of burns focus on clinical variables such as age or the body surface area. However, the evolution of other parameters (e.g. diuresis or fluid balance) during the first days is also valuable knowledge. In this work we suggest a methodology and we propose a Temporal Data Mining algorithm to estimate the survival condition from the patient’s evolution. Experiments conducted on 480 patients show the improvement of survival prediction. Ministerio de Economía y Competitividad (TIN2013-45491-R) Instituto de Salud Carlos III (FIS PI 12/2898) European Fund for Regional Development (EFRD) 0.252 SJR (2015) Q3, 155/444 Computer science (miscellaneous); Q4, 105/145 Theoretical computer science UEM |
| Identificador |
Casanova, I. J., Campos, M., Juárez, J. M., Fernández Fernández-Arroyo, A., & Lorente, J. Á. (2015). Using multivariate sequential patterns to improve survival prediction in intensive care burn unit. In Conference on Artificial Intelligence in Medicine in Europe (pp. 277-286). Lecture Notes in Computer Science, 9105. DOI: 10.1007/978-3-319-19551-3_36 9783319195506 9783319195513 03029743 http://hdl.handle.net/11268/6126 10.1007/978-3-319-19551-3_36 |
| Idioma(s) |
eng |
| Publicador |
Springer International Publishing |
| Direitos |
openAccess |
| Palavras-Chave | #Quemaduras #Enfermos críticos #Paciente #Salud |
| Tipo |
conferenceObject |
