Prediction of 30-Day Mortality after a Hip Fracture Surgery Using Neural and Bayesian Networks
作者： Dimitrios Galiatsatos , George C. Anastassopoulos , Georgios Drosos , Athanasios Ververidis , Konstantinos Tilkeridis
DOI: 10.1007/978-3-662-44654-6_56
关键词:
摘要: Osteoporotic hip fractures have a significant morbidity and excess mortality among the elderly imposed huge health economic burdens on societies worldwide. A medical database of 349 patients that been operated for fracture has analyzed. Two models data were used in Multi-Layer Perceptrons, Radial Basis Function Naive Bayes networks, order to predict 30-day after surgery also investigate which is most appropriate risk factor between New Mobility Score Institution Greek population. The proposed method may be as screening tool will assist orthopedics according each different patient.
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