Developing a Bayesian belief network for the management of geriatric hospital care

Health Care Management Science

Volumn 4, Issue 1, 2001, Pages 25-30

  Marshall, A H ^a   McClean, S I ^a   Shapcott, C M ^a   Hastie, I R ^b   Millard, P H ^b  

^a UNIVERSITY OF ULSTER   (United Kingdom)

^b ST GEORGE S HOSPITAL   (United Kingdom)

Author keywords

Bayesian belief networks; Duration of stay; Geriatric patients

Indexed keywords

AGED; ARTICLE; BAYES THEOREM; COMPUTER PROGRAM; EPIDEMIOLOGY; GERIATRIC ASSESSMENT; HOSPITAL DISCHARGE; HUMAN; LENGTH OF STAY; ORGANIZATION AND MANAGEMENT; PUBLIC HOSPITAL; STATISTICS; TREATMENT OUTCOME; UNITED KINGDOM; UTILIZATION REVIEW; VALIDATION STUDY;

AGED; BAYES THEOREM; CAUSALITY; GERIATRIC ASSESSMENT; HOSPITALS, PUBLIC; HUMANS; LENGTH OF STAY; LONDON; OUTCOME AND PROCESS ASSESSMENT (HEALTH CARE); PATIENT DISCHARGE; SOFTWARE;

EID: 0035257854     PISSN: 13869620     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1009645531478     Document Type: Article

References (14)

1. F.V. Jensen, An Introduction to Bayesian Networks (UCL Press Ltd., 1996).
2. E.J. Dunstan, K. Amar, A. Watt and D.G. Seymour, First steps in building ACME - an admission case-mix system for the elderly, Age & Ageing 25 (1996) 102-108.
3. B. Bertozzi, P. Barbisoni, S. Franzoni, R. Rozzini, G.B. Frisoni and M. Trabucchi, Factors related to length of stay in a geriatric evaluation and rehabilitation unit, Aging (Milano) 8 (1996) 170-175.
4. K. Kafetz, J. O'Farrell, A. Parry, V. Wijesuriya, G. McElligott, B. Rossiter and M. Lugon, Age-related geriatric medicine: relevance of special skills of geriatric medicine to elderly people admitted to hospital as medical emergencies, Journal of the Royal Society of Medicine 88 (1995) 629-633.
5. P.R. Cox, Demography (Cambridge University Press, 1976).
6. F.I. Mahony and D.W. Barthel, Functional evaluation: the Barthel index, Maryland State Medical Journal 14 (1965) 61-65.
7. D. Cox and N. Wermuth, Multivariate Dependencies (Chapman and Hall, London, 1996).
8. W. Buntine, A guide to the literature on learning probabilistic networks from data, IEEE Transactions on Knowledge and Data Engineering 8(1996) 195-210.
9. D. Heckerman, Bayesian Networks for Knowledge Discovery. Advances in Knowledge Discovery, eds. U.M. Fayyad et al. (AAAI Press/MIT Press, 1996).
10. S.L. Lauritzen and N. Wermuth, Graphical models for associations between variables, some of which are qualitative and some quantitative, The Annals of Statistics 17 (1989) 31-57.
11. J.H. Badsberg, A Guide to CoCo - An Environment for Graphical Models (Institute of Electronic Systems, Department of Mathematics and Computer Science, Aalborg University, Denmark, 1992).
12. P.M. Sagar, M.N. Hartley, J. MacFie, B.A. Taylor and G.P. Copeland, Comparison of individual surgeon's performance. Risk-adjusted analysis with POSSUM scoring system, Diseases of the Colon & Rectum 39 (1996) 654-658.
13. A.H. Marshall, S.I. McClean, C.M. Shapcott and P.H. Millard, Using Bayesian belief networks to predict the survival of stroke patients, in: Proceedings of the IX International Symposium on Applied Stochastic Models and Data Analysis (University of Lisbon, Portugal, 1999) pp. 112-117.
14. A.H. Marshall, S.I. McClean, C.M. Shapcott and P.H. Millard, Learning dynamic Bayesian belief networks using conditional phase-type distributions, in: Lecture Notes in Artificial Intelligence, Vol. 1910, eds. D.A. Zighed, J. Komorowski and J. Zytkow (Springer, 2000).