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Prediction of 30-Day Mortality after a Hip Fracture Surgery Using Neural and Bayesian Networks

  • Dimitrios Galiatsatos
  • George C. Anastassopoulos
  • Georgios Drosos
  • Athanasios Ververidis
  • Konstantinos Tilkeridis
  • Konstantinos Kazakos
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 436)

Abstract

Osteoporotic hip fractures have a significant morbidity and excess mortality among the elderly and have imposed huge health and economic burdens on societies worldwide. A medical database of 349 patients that have been operated for hip fracture has been analyzed. Two models of data were used in Multi-Layer Perceptrons, Radial Basis Function and Naïve Bayes networks, in order to predict the 30-day mortality after a hip fracture surgery and also to investigate which is the most appropriate risk factor between the New Mobility Score and Institution factor for the Greek population. The proposed method may be used as a screening tool that will assist orthopedics in the surgery of the hip fracture according to each different patient.

Keywords

hip fracture artificial neural networks Bayesian networks 30-day mortality 

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Copyright information

© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  • Dimitrios Galiatsatos
    • 1
  • George C. Anastassopoulos
    • 1
  • Georgios Drosos
    • 2
  • Athanasios Ververidis
    • 2
  • Konstantinos Tilkeridis
    • 1
  • Konstantinos Kazakos
    • 2
  1. 1.Medical Informatics Laboratory, Medical SchoolDemocritus University of ThraceGreece
  2. 2.Department of OrthopedicsUniversity Hospital of Alexandroupolis, Medical School, Democritus University of ThraceGreece

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