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

Health Care Management Science
Jaakko RiihimäkiAki Vehtari

Abstract

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.

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Citations

Jun 10, 2011·Annals of Medicine·Unto HäkkinenReijo Sund
Jun 10, 2011·Annals of Medicine·Reijo SundUnto Häkkinen
Jun 11, 2015·Journal of Evaluation in Clinical Practice·Inmaculada Hernandez, Yuting Zhang
Nov 5, 2014·JAMA Internal Medicine·Inmaculada HernandezYuting Zhang
Apr 22, 2016·Osteoporosis International : a Journal Established As Result of Cooperation Between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA·K ThorneS E Roberts

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