Abstract
This paper is concerned with handling uncertainty as part of the analysis of data from a medical study. The study is investigating connections between the birth weight of babies and the dietary intake of their mothers. Bayesian belief networks were used in the analysis. Their perceived benefits include (i) an ability to represent the evidence emerging from the evolving study, dealing effectively with the inherent uncertainty involved; (ii) providing a way of representing evidence graphically to facilitate analysis and communication with clinicians; (iii) helping in the exploration of the data to reveal undiscovered knowledge; and (iv) providing a means of developing an expert system application.
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References
Stein, Z., Fusser, M.: The Dutch Famine, 1944-45, and the Reproductive Process. Pediatric Research 9 (1975) 70–76
Doyle, W. Crawford, M.A., Wynn, A.H.A., Wynn, S.W.: The Association Between Maternal Diet and Birth Dimensions. Journal of Nutrition and Medicine 1 (1990) 9–17
Olsen, S.F., Olsen, J., Frische, G.: Does Fish Consumption During Pregnancy Increase Fetal Growth? International Journal of Epidemiology 19 (1990) 971–977
Bakketeig, L.S., Hoffman, H.J., Titmuss Oakley, A.R.: Perinatal Mortality. In: Bracken M.B. (ed.): Perinatal Epidemiology, Oxford University Press, New York, Oxford (1984) 99–151
Walther, F.J., Raemaekers, L.H.J.: Neonatal Morbidity of SGA Infants in Relation to their Nutritional Status at Birth. Acta Paediatric Research Scandia 71 (1982) 437–440
Barker, D.J.P.: The Fetal and Infant Origins of Adult Disease. British Medical Journal, London (1992)
Mathews, F., Neil, H.A.W.: Nutrient Intakes During Pregnancy in a Cohort of Nulliparous Women. Journal of Human Nutrition and Dietetics 11(1998) 151–161
Pettitt, D.J., Knowler, W.C., Baird, H.R., Bennett, P.H.: Gestational Diabetes: Infant and Maternal Complications in Relation to 3rd Trimester Glucose Tolerance in Pima Indians. Diabetes Care, 3 (1980) 458–464
Sermer, M., Naylor, C.D., Gare, D.J.: Impact of Increasing Carbohydrate Intolerance on Maternal-fetal Intrauterine Growth Retardation. Human Nutrition and Clinical Nutrition 41C (1995) 193–197
Cooper, G.F.: Current Research Directions in the Development of Expert Systems Based on Belief Networks. Applied Stochastic Models and Data Analysis 5 (1989) 39–52
Millar, R.A.: Medical Diagnostic DSS-Past, Present and Future. JAMIA 1 (1994) 8–27
Bratko, I., Muggleton, S.: Applications of Inductive Logic Programming. Comm. ACM 38(11) (1995) 65–70
Lisboa, P.J.G., Ifeachor, E.C., Szczepaniak P.S. (eds.): Artificial Neural Networks in Biomedicine. Springer (2000)
Andersen, L.R., Krebs, J.H., Damgaard, J.: STENO: An Expert System for Medical Diagnosis Based on Graphical Models and Model Search. Journal of Applied Statistics 18 (1991) 139–153
Madigan, D.: Bayesian Graphical Models for Discrete Data. Technical Report, University of Washington, Seattle (1993)
Lauritzen, S.L., Spiegelhalter, D.J.: Local Comparisons with Probabilities on Graphical Structures and their Application to Expert Systems. Journal Royal Statistical Society B 50(2) (1988) 157–224
Korver, M., Lucas, P.J.F.: Converting a Rule-Based Expert System into a Belief Network. Med. Inform. 18(3) (1993) 219–241
Buntine, W.: Graphical Models for Discovering Knowledge. In: Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusay, R. (eds.): Advances in Knowledge Discovery, AAAI Press / The MITPress (1996) 59–82
Ramoni, M., Sebastiani, P.: Bayesian Methods for Intelligent Data Analysis. In: Berthold, M., Hand, D.J. (eds.): Intelligent Data Analysis: An Introduction, Springer, New York (1999)
Cox, D.R., Wermuth N.: Multivariate Dependencies, Chapman and Hall (1996)
Hojsgaard, S., Thiesson, B., Greve, J., Skjoth, F.: BIROST-A Program for Inducing Block Recursive Models from a Complete Database, Institute of Electronic Systems, Department of Mathematics and Computer Science, Aalborg University, Denmark (1992)
Spiegelhalter, D.J., Dawid, A.P., Lauritzen, S.L., Cowell, R.G.: Bayesian Analysis in Expert Systems, Statistical Science 8(3) (1993) 219–283
Cheng, J., Bell, D.A., Liu, W.: An Algorithm for Bayesian Network Construction from Data. 6th International Workshop on AI and Stats (1997) 83–90
Chow, C. J. K., Liu, C. N.: Approximating Discrete Probability Distributions with Dependence Trees, IEEE Trans. Information Theory, Vol. 14(3), (1968) 462–467
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Marshall, A., Bell, D., Sterritt, R. (2002). Handling Uncertainty in a Medical Study of Dietary Intake during Pregnancy. In: Bustard, D., Liu, W., Sterritt, R. (eds) Soft-Ware 2002: Computing in an Imperfect World. Soft-Ware 2002. Lecture Notes in Computer Science, vol 2311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46019-5_16
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DOI: https://doi.org/10.1007/3-540-46019-5_16
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