Abstract
We present the ProCarSur system for prognostic reasoning in the domain of cardiac surgery. The system has a three-tiered architecture consisting of a Bayesian network, a task layer, and a graphical user interface. In contrast to traditional prognostic tools, that are usually based on logistic regression, ProCarSur implements a dynamic, process-oriented view on prognosis. The system distinguishes between the various phases of peri-surgical care, explicates the scenarios that lead to different clinical outcomes, and can be used to update predictions when new information becomes available. To support users in their interaction with the Bayesian network, a set of predefined prognostic reasoning tasks is implemented in the task layer. The user communicates with the system through an interface that hides the underlying Bayesian network and aggregates the results of probabilistic inferences.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Wyatt, J., Altman, D.G.: Prognostic models: clinically useful or quickly forgotten? Br. Med. J 311, 1539–1541 (1995)
Abu-Hanna, A., Lucas, P.J.F.: Prognostic models in medicine. Methods of Information in Medicine 40, 1–5 (2001)
DesHarnais, S.I., Forthman, M.T., Homa-Lowry, J.M., Wooster, L.D.: Risk-adjusted quality outcome measures: indexes for benchmarking rates of mortality, complications, and readmissions. Qual Manag Health Care 5(2), 80–87 (1997)
http://hp2010.nhlbihin.net/atpiii/calculator.asp (Last accessed April 17, 2007)
Kannel, W.B.: Fifty years of Framingham Study contributions to understanding hypertension. J Hum. Hypertension 14, 83–90 (2000)
http://www.icumedicus.com/icu_scores/apacheIV.php (Last accessed April 17, 2007)
Zimmerman, J.E., Kramer, A.A., McNair, D.S., Malila, F.M.: Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today’s critically ill patients. Crit. Care Med. 34(5), 1297–1310 (2006)
www.euroscore.org/calc.html (Last accessed April 17, 2007)
Nashef, S.A.M., Roques, F., Michel, P., Gauducheau, E., Lemeshow, S., Salomon, R.: European system for cardiac operative risk evaluation (EuroSCORE). European Journal of Cardio-Thoracic Surgery 16, 9–13 (1999)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo, CA (1988)
Cowell, R.G., Dawid, A.P., Lauritzen, S.L., Spiegelhalter, D.J.: Probabilistic Networks and Expert Systems. Springer, New York (1999)
Verduijn, M., Rosseel, P.J.M., Peek, N., de Jonge, E., de Mol, B.A.J.M.: Prognostic Bayesian networks. I: Rationale, learning procedure, and clinical use. II: An application in the domain of cardiac surgery (submitted for publication)
Metz, C.E.: Basic principles of ROC analysis. Sem. Nucl. Med. 8, 283–298 (1978)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Peek, N., Verduijn, M., Sjoe-Sjoe, W.G.T., Rosseel, P.J.M., de Jonge, E., de Mol, B.A.J.M. (2007). ProCarSur: A System for Dynamic Prognostic Reasoning in Cardiac Surgery. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds) Artificial Intelligence in Medicine. AIME 2007. Lecture Notes in Computer Science(), vol 4594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73599-1_45
Download citation
DOI: https://doi.org/10.1007/978-3-540-73599-1_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73598-4
Online ISBN: 978-3-540-73599-1
eBook Packages: Computer ScienceComputer Science (R0)