2026-04-27T21:50:40Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/85772024-02-08T01:05:13Zcom_10259_3847com_10259_5086com_10259_2604col_10259_7109col_10259_7409

Complications Detection in Treatment for Bacterial Endocarditis Curiel Herrera, Leticia Elena Baruque Zanón, Bruno Dueñas, Carlos Corchado, Emilio Pérez, Cristina Decision Tree Infective Endocarditis Bacterial Endocarditis Kernel Cluster Case Base Reasoning System Informática Medicina Computer science Medicine Trabajo presentado en: International Symposium on Distributed Computing and Artificial Intelligence, 2011 This study proposes the use of decision trees to detect possible complications in a critical disease called endocarditis. The endocarditis illness could produce heart failure, stroke, kidney failure, emboli, immunological disorders and death. The aim is to obtained a tree decision classifier based on the symptoms (attributes) of patients (the data instances) observed by doctors to predict the possible complications that can occur when a patient is in treatment of bacterial endocarditis and thus, help doctors to make an early diagnose so that they can treat more effectively the infection and aid to a patient faster recovery. The results obtained using a real data set, show that with the information extracted form each case in an early stage of the development of the patient a quite accurate idea of the complications that can arise can be extracted. 2024-02-05T10:48:22Z 2024-02-05T10:48:22Z 2011 info:eu-repo/semantics/bookPart info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/acceptedVersion 1867-5662 http://hdl.handle.net/10259/8577 10.1007/978-3-642-19934-9_30 1867-5670 eng Advances in Intelligent and Soft Computing. 2011, V. 91, p. 241-248 https://doi.org/10.1007/978-3-642-19934-9_30 info:eu-repo/semantics/openAccess application/pdf Springer Nature