RESUMEN
An abdominal aortic aneurysm is an abnormal dilatation of the aortic vessel at abdominal level. This disease presents high rate of mortality and complications causing a decrease in the quality of life and increasing the cost of treatment. To estimate the mortality risk of patients undergoing surgery is complex due to the variables associated. The use of clinical decision support systems based on machine learning could help medical staff to improve the results of surgery and get a better understanding of the disease. In this work, the authors present a predictive system of inhospital mortality in patients who were undergoing to open repair of abdominal aortic aneurysm. Different methods as multilayer perceptron, radial basis function and Bayesian networks are used. Results are measured in terms of accuracy, sensitivity and specificity of the classifiers, achieving an accuracy higher than 95%. The developing of a system based on the algorithms tested can be useful for medical staff in order to make a better planning of care and reducing undesirable surgery results and the cost of the post-surgical treatments.
Asunto(s)
Aneurisma de la Aorta Abdominal/cirugía , Mortalidad Hospitalaria , Aprendizaje Automático , Medición de Riesgo , Aneurisma de la Aorta Abdominal/mortalidad , Teorema de Bayes , Humanos , Calidad de Vida , Factores de Riesgo , Resultado del TratamientoRESUMEN
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.
Asunto(s)
Procedimientos Quirúrgicos Cardíacos , Técnicas de Apoyo para la Decisión , Cardiopatías Congénitas/cirugía , Algoritmos , Cardiología/métodos , Toma de Decisiones Clínicas , Árboles de Decisión , Femenino , Humanos , Lactante , Recién Nacido , Funciones de Verosimilitud , Aprendizaje Automático , Masculino , Modelos Estadísticos , Redes Neurales de la Computación , Reproducibilidad de los Resultados , RiesgoRESUMEN
Particle Swarm Optimization is an optimization technique based on the positions of several particles created to find the best solution to a problem. In this work we analyze the accuracy of a modification of this algorithm to classify the levels of risk for a surgery, used as a treatment to correct children malformations that imply congenital heart diseases.