A comparison of learning algorithms for Bayesian networks: a case study based on data from an emergency medical service

Due to the uncertainty of many of the factors that influence the performance of an emergency medical service, we propose using Bayesian networks to model this kind of system. We use different algorithms for learning Bayesian networks in order to build several models, from the hospital manager's point of view, and apply them to the specific case of the emergency service of a Spanish hospital. This first study of a real problem includes preliminary data processing, the experiments carried out, the comparison of the algorithms from different perspectives, and some potential uses of Bayesian networks for management problems in the health service.

[1]  Gregory F. Cooper,et al.  An Entropy-driven System for Construction of Probabilistic Expert Systems from Databases , 1990, UAI.

[2]  P. Spirtes,et al.  Causation, prediction, and search , 1993 .

[3]  David Maxwell Chickering,et al.  Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.

[4]  Luis M. de Campos,et al.  A new approach for learning belief networks using independence criteria , 2000, Int. J. Approx. Reason..

[5]  Solomon Kullback,et al.  Information Theory and Statistics , 1960 .

[6]  Gregory F. Cooper,et al.  A Bayesian Method for the Induction of Probabilistic Networks from Data , 1992 .

[7]  P H Millard,et al.  Developing a Bayesian belief network for the management of geriatric hospital care , 2001, Health care management science.

[8]  Mtw,et al.  Computation, causation, and discovery , 2000 .

[9]  Luis M. de Campos,et al.  A hybrid methodology for learning belief networks: BENEDICT , 2001, Int. J. Approx. Reason..

[10]  R. Bouckaert Minimum Description Length Principle , 1994 .

[11]  Judea Pearl,et al.  Equivalence and Synthesis of Causal Models , 1990, UAI.

[12]  R. Bouckaert Bayesian belief networks : from construction to inference , 1995 .

[13]  J. Pearl Causality: Models, Reasoning and Inference , 2000 .

[14]  Wray L. Buntine Theory Refinement on Bayesian Networks , 1991, UAI.

[15]  Luis M. de Campos,et al.  An Algorithm for Finding Minimum d-Separating Sets in Belief Networks , 1996, UAI.

[16]  Silvia Acid Carrillo Métodos de aprendizaje de redes de creencia. Aplicación a la clasificación , 1999 .

[17]  Wai Lam,et al.  LEARNING BAYESIAN BELIEF NETWORKS: AN APPROACH BASED ON THE MDL PRINCIPLE , 1994, Comput. Intell..

[18]  Solomon Kullback,et al.  Information Theory and Statistics , 1970, The Mathematical Gazette.

[19]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[20]  Jie Cheng,et al.  An Algorithm for Bayesian Belief Network Construction from Data , 2004 .

[21]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[22]  Marek J. Druzdzel,et al.  A Hybrid Anytime Algorithm for the Construction of Causal Models From Sparse Data , 1999, UAI.

[23]  Luis M. de Campos,et al.  Independency relationships and learning algorithms for singly connected networks , 1998, J. Exp. Theor. Artif. Intell..

[24]  Remco R. Bouckaert,et al.  Probalistic Network Construction Using the Minimum Description Length Principle , 1993, ECSQARU.

[25]  Moninder Singh,et al.  Construction of Bayesian network structures from data: A brief survey and an efficient algorithm , 1995, Int. J. Approx. Reason..

[26]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.