Akademik Veri Yönetim Sistemi
Proceedings of the International Conference on Data Science, Machine Learning and Statistics (DMS-2019), Van, Türkiye, 26 - 29 Haziran 2019, ss.8-11, (Tam Metin Bildiri)
MIMIC-III is a data set composed of more than 60000 admissions made to Beth Israel Hospitals. For every deidentified critical care patients demographics, vital signs, laboratory tests, medications, and more are hold in this database. The most important cause of deaths in the hospital is considered as Sepsis. Sepsis is defined as ‘lifethreatening organ dysfunction caused by a dysregulated host response to infections. In medical literature, many scoring systems such as SOFA, LODS, SIRS, NEWS, etc. have been suggested for the early prediction/diagnosis of sepsis and evaluation of prognosis. Both machine learning and statistical learning methods have been applied to model survival/death status for intensive care unit patients in Mimic - III database. Used methods are Random Forest, Support Vector Machine, Logistic Regression, Naive Bayes, Adaboost and Artificial Neural Networks (ANN). It is a well-known fact that ANN approach is an effective prediction tool. And, it is very crucial to determine the best ANN model in order to get accurate predictions. In this study, different ANN models have been applied to MIMICIII data set to determine the best ANN model. As a result of the implementation, all obtained prognostic results are
presented and discussed.