Health Care Management Science

, Volume 13, Issue 2, pp 170–181 | Cite as

Analysing the length of care episode after hip fracture: a nonparametric and a parametric Bayesian approach



Effective utilisation of limited resources is a challenge for health care providers. Accurate and relevant information extracted from the length of stay distributions is useful for management purposes. Patient care episodes can be reconstructed from the comprehensive health registers, and in this paper we develop a Bayesian approach to analyse the length of care episode after a fractured hip. We model the large scale data with a flexible nonparametric multilayer perceptron network and with a parametric Weibull mixture model. To assess the performances of the models, we estimate expected utilities using predictive density as a utility measure. Since the model parameters cannot be directly compared, we focus on observables, and estimate the relevances of patient explanatory variables in predicting the length of stay. To demonstrate how the use of the nonparametric flexible model is advantageous for this complex health care data, we also study joint effects of variables in predictions, and visualise nonlinearities and interactions found in the data.


Length of stay Hip fracture Bayesian analysis Multilayer perceptron Weibull mixture Covariate relevance 


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of Biomedical Engineering and Computational ScienceHelsinki University of Technology—TKKHelsinkiFinland
  2. 2.Service Systems Research UnitNational Institute for Health and Welfare-THLHelsinkiFinland

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