Abstract
In current high-dependency clinical environments such as Intensive Coronary Care Unit (ICCU hereinafter), operating rooms and so on, the clinical staff is presented with a large mass of data about the patient’s state. These data can be obtained from the advanced biomedical equipment (especially from electrical and hemodynamical monitors), patient’s history, physical examination findings and test results. This massive flow of information can lead to some well-known problems such as data overload and missing data and misinterpretation [1,13]. In order to avoid these kinds of problems, Intelligent Patient Supervision Systems (IPSS hereinafter) have been developed. IPSSs must be developed to support the interpretation of these data and they should provide information in higher abstraction levels in order to improve the decision making process.
This paper was presented as a research work carried out in the project Temporal Information Management and Intelligent Interaction in Medicine (TIC95-0604-C02-01) supported by the Spanish CICYT
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
J. J. Van Der AA. Intelligent Alarms in Anaesthesia: a Real Time Expert System Application. PhD thesis, Technical University of Eindhoven, 1990.
S. Barro, R. Marín, R. P. Otero, R. Ruíz, and J. Mira. On the handling of time in intelligent monitoring of CCU patients. In Proceedings of the 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 871–873, 1992.
S. Barro, R. Marín, J. Mira, and A. R. Patón. A model and a language for the fuzzy representation and handling of time. Fuzzy Sets and Systems, 61, 153–175, 1994.
V. R. Benjamins. Problem Solving Methods for Diagnosis. PhD thesis, University of Amsterdam, 1993.
V. Brusoni, L. Console, P. Terenziani, and D. Theseider Dupré. A spectrum of definitions for temporal model-based diagnosis. Artificial Intelligence, 102, 39–79, 1998.
M. A. Cárdenas. A Constraint-Based Logic Model for Rerepresenting and Managing Temporal Information. (In Spanish). PhD thesis, Universidad de Murcia, 1998.
M. A. Cárdenas, R. Marín, I. Navarrete, and M. Balsa. Fuzzy temporal constraint logic: A valid resolution principle. Fuzzy Sets and Systems, 117(2), 231–250, 2000.
M. J. Chantier, G. M. Coghill, Q. Shen, and R. R. Leitch. Selecting tools and techniques for model-based diagnosis. Artificial Intelligence in Engineering, 12, 81–98, 1998.
L. Console and P. Torasso. On co-operation between abductive and temporal reasoning in medical diagnosis. Artificial Intelligence in Medicine, 3, 291–311, 1991.
L. Console, A. J. Rivolin, and P. Torraso. Fuzzy temporal reasoning on causal models. International Journal of Intelligent Systems, 6, 107–133, 1991.
L. Console and P. Torraso. A spectrum of logical definitions of model-based diagnosis. In Walter Hamscher, Luca Console, and Johan de Kleer, editors, Readings in Model-Based Diagnosis, 78–88. Morgan Kauffmann Publisher, Inc., 1992.
L. Console, L. Protinale, and D. T. Dupré. Using compiled knowledge to guide focus abductive diagnosis. IEEE Transactions on Knowledge and Data Engineering, 8(5), 690–706, 1996.
M. Dojat and C. Sayettat. Realistic model for temporal reasoning in real-time patient monitoring. Applied Artificial Intelligence, 10, 121–143, 1996.
M. Dojat, N. Ramaux, and D. Fontaine. Scenario recognition for temporal reasoning in medical domains. Artificial Intelligence in Medicine, 14, 139–155, 1999.
L. Eshelman. MOLE: A knowledge-acquisition tool for cover-and-differentiate systems. In S. Marcus, editor, Automating Knowledge Acquisition for Expert Systems, 37–80. Kluwer, Boston, 1988.
S. Fraga, P. Félix, M. Lama, E. Sanchez, and S. Barro. A proposal for a real time signal perception specialist. In International Symposium on Engineering of Intelligent Systems EIS’98, 3, 261–267, 1998.
J. Gamper and W. Nejdl. Abstract temporal diagnosis in medical domains. Artificial Intelligence in Medicine, 10(3), 1116–1122, 1997.
Ira J. Haimowitz and Isaac S. Kohane. Managing temporal worlds for medical trend diagnosis. Artificial Intelligence in Medicine, 8, 299–321, 1996.
W. Hamscher, L. Console, and J. de Kleer. Readings in Model-Based Diagnosis. Morgan Kauffman, San Mateo, 1992.
W. J. Long. Evaluation of a new method for cardiovascular reasoning. Journal of the American Medical Informatics Association, 1, 127–141, 1994.
W. Long. Temporal reasoning for diagnosis in causal probabilistic knowledge base. Artificial Intelligence in Medicine, 8, 193–215, 1996.
R. Marín, S. Barro A. Bosch, and J. Mira. Modeling time representation from a fuzzy perspective. Cybernetics and Systems, 25 (2), 207–215, 1994.
R. Marín, M. Balsa M. A. Cárdenas, and J. L. Sanchez. Obtaining solutions in fuzzy constraint networks. International Journal of Approximate Reasoning, 34, 261–288, 1996.
A. A. F. Van der Maas, A. H. M. Ter Hofstede, and P. F. de Vries Robbé. Formal description of temporal knowledge in case report. Artificial Intelligence in Medicine, 16, 251–282, 1999.
W. Nejdl and J. Gamper. Harnessing the power of temporal abstractions in model-based diagnosis of dynamic systems. In Proceedings of the 11th ECAI, 667–671, Amsterdam, 1994.
J. T. Palma, R. Marín, J. L. Sanchez, and M. A. Cárdenas. A diagnosis task in an intelligent patient supervisory system. In Proc. of the XV IFIP World Computer Congress-Information Technologies and Knowledge-based Systems IT&KNOWS’98, 159–172, Vienna-Budapest, 1998.
J. T. Palma. Applying Knowledge Engineering to Real-Time Knowledge Based Systems: A CommonKADS Extension (in Spanish). PhD thesis, Universidad de Murcia, 1999.
T. Peng and J. Reggia. Abductive Inference Methods for Diagnositic Problem Solving. Springer-Verlag, Berlin, 1991.
A. T. Schreiber. Pragmatics of the Knowledge Level. PhD thesis, University of Amsterdam, 1992.
Y. Shahar and M. Musen. Résumé: A temporal-abstraction system for patient monitoring. Computers and Biomedical Research, 26, 255–273, 1993.
F. Steimann and K. P. Adlassing. Clinical monitoring with fuzzy automata. Fuzzy Set and Systems, 61, 37–42, 1994.
F. Steimann and K. P. Adlassnig. A fuzzy medical data model. In Proceedings of the 12th European Meeting on Cybernetics and Systems Research, 271–278, Singapore, 1994. World Scientific.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Palma, J.T., Marín, R., Sánchez, J.L., Palacios, F. (2002). A Model-based Temporal Abductive Diagnosis Model for an Intensive Coronary Care Unit. In: Barro, S., Marín, R. (eds) Fuzzy Logic in Medicine. Studies in Fuzziness and Soft Computing, vol 83. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1804-8_9
Download citation
DOI: https://doi.org/10.1007/978-3-7908-1804-8_9
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-2498-8
Online ISBN: 978-3-7908-1804-8
eBook Packages: Springer Book Archive