Aid decision algorithms to estimate the risk in congenital heart surgery
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http://hdl.handle.net/10045/53439
Título: | Aid decision algorithms to estimate the risk in congenital heart surgery |
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Autor/es: | Ruiz-Fernandez, Daniel | Monsalve Torra, Ana | Soriano Payá, Antonio | Marin-Alonso, Oscar | Triana Palencia, Eddy |
Grupo/s de investigación o GITE: | Ingeniería Bioinspirada e Informática para la Salud |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Tecnología Informática y Computación |
Palabras clave: | Artificial neural networks | Classifiers | Congenital heart disease | Data analysis | Decision trees |
Área/s de conocimiento: | Arquitectura y Tecnología de Computadores |
Fecha de publicación: | abr-2016 |
Editor: | Elsevier |
Cita bibliográfica: | Computer Methods and Programs in Biomedicine. 2016, 126: 118-127. doi:10.1016/j.cmpb.2015.12.021 |
Resumen: | Background and objective: In this paper, we have tested the suitability of using different artificial intelligence-based algorithms for decision support when classifying the risk of congenital heart surgery. In this sense, classification of those surgical risks provides enormous benefits as the a priori estimation of surgical outcomes depending on either the type of disease or the type of repair, and other elements that influence the final result. This preventive estimation may help to avoid future complications, or even death. Methods: We have evaluated four machine learning algorithms to achieve our objective: multilayer perceptron, self-organizing map, radial basis function networks and decision trees. The architectures implemented have the aim of classifying among three types of surgical risk: low complexity, medium complexity and high complexity. Results: Accuracy outcomes achieved range between 80% and 99%, being the multilayer perceptron method the one that offered a higher hit ratio. Conclusions: According to the results, it is feasible to develop a clinical decision support system using the evaluated algorithms. Such system would help cardiology specialists, paediatricians and surgeons to forecast the level of risk related to a congenital heart disease surgery. |
URI: | http://hdl.handle.net/10045/53439 |
ISSN: | 0169-2607 (Print) | 1872-7565 (Online) |
DOI: | 10.1016/j.cmpb.2015.12.021 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2015 Elsevier Ireland Ltd. |
Revisión científica: | si |
Versión del editor: | http://dx.doi.org/10.1016/j.cmpb.2015.12.021 |
Aparece en las colecciones: | INV - IBIS - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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2016_Ruiz-Fernandez_etal_CMPB_final.pdf | Versión final (acceso restringido) | 517,42 kB | Adobe PDF | Abrir Solicitar una copia |
2016_Ruiz-Fernandez_etal_CMPB_accepted.pdf | Accepted Manuscript (acceso abierto) | 514,89 kB | Adobe PDF | Abrir Vista previa |
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