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

被引:54
作者
Acid, S
de Campos, LM
Fernández-Luna, JM
Rodríguez, S
Rodríguez, JM
Salcedo, JL
机构
[1] Univ Granada, Escuela Tecn Super Ingn Informat, Dept Ciencias Computac & IA, E-18071 Granada, Spain
[2] Univ Jaen, Dept Informat, Jaen, Spain
[3] Hosp Univ Virgen Nieves Granada, Granada, Spain
关键词
Bayesian networks; learning algorithm; scoring functions; independence; emergency medical service; management decision support in the health service;
D O I
10.1016/j.artmed.2003.11.002
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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. (C) 2004 Elsevier B.V. All rights reserved.
引用
收藏
页码:215 / 232
页数:18
相关论文
共 27 条
  • [11] Cheng J, 1999, UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, P101
  • [12] Cheng J., 1997, P AI STAT 97, P83
  • [13] COOPER GF, 1992, MACH LEARN, V9, P309, DOI 10.1007/BF00994110
  • [14] Dash D, 1999, UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, P142
  • [15] Independency relationships and learning algorithms for singly connected networks
    De Campos, LM
    [J]. JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE, 1998, 10 (04) : 511 - 549
  • [16] de Campos LM, 2001, P 3 INT S AD SYST EV, P109
  • [17] Bayesian network classifiers
    Friedman, N
    Geiger, D
    Goldszmidt, M
    [J]. MACHINE LEARNING, 1997, 29 (2-3) : 131 - 163
  • [18] Glymour C., 1999, Computation, Causation, and Discovery
  • [19] Glymour C., 1993, LECT NOTES STAT, V1, DOI 10.1007/978-1-4612-2748-9
  • [20] LEARNING BAYESIAN NETWORKS - THE COMBINATION OF KNOWLEDGE AND STATISTICAL-DATA
    HECKERMAN, D
    GEIGER, D
    CHICKERING, DM
    [J]. MACHINE LEARNING, 1995, 20 (03) : 197 - 243