Abstract
This study presents the application of Bayesian networks (Bn) to explain Neonatal Intensive Care Unit relationships. Information was compiled retrospectively from the medical records at two neonatal intensive care units of 523 neonates (63 deaths). A total of 31 variables were used for the model, eleven to characterize admission conditions and severity of illness as well as the 20 technologies. With mortality as the output variable, the K2 search algorithm and Geiger-Heckerman quality measures were used in the training that generated the Bn. Evidence propagation was used to assess the training, which yielded a sensitivity of 77.78% and a specificity of 91.30%, in the classification of mortality. Clinical criteria, correlations and logistical regression were used to analyse the relationships the model provided. The Bn found clinically coherent relationships as recognizable conditions that directly affect mortality such as congenital malformations are seen and it exposes the least effective technologies among those studied, bicarbonate treatment.
This study has been supported by FONDECYT (Chile) project No 1990920 and DICYT – USACH.
Chapter PDF
Similar content being viewed by others
Keywords
- Intensive Care Unit
- Bayesian Network
- Neonatal Intensive Care Unit
- Congenital Malformation
- Causal Network
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Evans, R.W.: Health care technology and the inevitability of resource allocation and rationing decisions. JAMA 249, 2047–2053 (1983)
Slonim, A.D., Patel, K.M., Ruttimann, U.E., Pollack, M.M.: The impact of prematurity: A perspective of pediatric intensive care units. Crit. Care Med. 28, 848–853 (2000)
Almeida, R.T., Panerai, R.B., Carvalho, M., Lopes, J.M.A.: Analysis of multiple technologies in neonatal care. Int. J. Techol. Assess. Health Care 7, 22–29 (1991)
Panerai, R.B., Chacón-Pacheco, M.L., Almeida, R.T.: Path Analysis in Health Technology Assessment. In: International Society of Technology Assessment in Health Care, Seventh Ann. Meet., Helsinki, Finland (1991)
Panerai, R.B., Almeida, R.T., Portela, M.C., Carvalho, M., Coura-Filho, M., Costa, T.P.: Estimating the effectiveness of perinatal care technologies by expert opinion. Int. J. Techol. Assess. Health Care 7, 367–378 (1991)
Kahn Jr., C.E., Roberts, L.M., Shaffer, K.A., Haddawy, P.: Construction of Bayesian Network for mammographic diagnosis of breast cancer. Comput. Biol. Med. 27(1), 19–29 (1997)
Sierra, B., Larranaga, P.: Predicting survival in malignant skin melanoma using Bayesian networks automatically induced by genetic algorithms. An empirical comparison between different approaches. Artif. Intell. Med. 14(1-2), 215–230 (1998)
Sierra, B., Serrano, N., Larranaga, P., Plasencia, E.J., Inza, I., Jimenez, J.J., Revuelta, P., Mora, M.L.: Using Bayesian networks in the construction of a bi-level multi-classifier. A case study using intensive care unit patients data. Artif. Intell. Med. 22(3), 233–248 (2001)
Heckerman, D.: Bayesian networks for knowledge discovery. In: Fayyad, U.M., Piatesky-Shapiro, G., Padhraic, S., Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 273–305. Mit Press, Melo Park (1996)
Castillo, E., Gutiérrez, J.M., Hadi, A.S.: Expert systems and probabilistic network models. Springer, New York (1997)
Gaiger, D., Heckerman, D.: A characterization of the Dirichlet distribution with application to learning Bayesian networks. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 196–207. Morgan Kaufmann Pub., San Francisco (1995)
Cooper, G.F., Herskovits, E.: A Bayesian Method for the induction of probabilistic networks from data. Machine Learning 9, 309–347 (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chacón, M., Maureira, B. (2004). Causal Networks for Modeling Health Technology Utilization in Intensive Care Units. In: Sanfeliu, A., Martínez Trinidad, J.F., Carrasco Ochoa, J.A. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2004. Lecture Notes in Computer Science, vol 3287. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30463-0_81
Download citation
DOI: https://doi.org/10.1007/978-3-540-30463-0_81
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23527-9
Online ISBN: 978-3-540-30463-0
eBook Packages: Springer Book Archive