Abstract
The temporal dimension is a characterizing factor of many diseases, in particular, of the exanthematic diseases. Therefore, the diagnosis of this kind of diseases can be based on the recognition of the typical temporal progression and duration of different symptoms. To this aim, we propose to apply a temporal reasoning system we have developed. The system is able to handle both qualitative and metric temporal knowledge affected by vagueness and uncertainty. In this preliminary work, we show how the fuzzy temporal framework allows us to represent typical temporal structures of different exanthematic diseases (e.g. Scarlet Fever, Measles, Rubella et c.) thus making possible to find matches with data coming from the patient disease.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
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
Abbod, M.F., von Keyserlinngk, D.G., Linkens, D.A., Mahfouf, M.: Survey of utilization of fuzzy technology in medicine and healthcare. Fuzzy Sets and Systems 120, 331–349 (2001)
Allen, J.F.: Maintaining knowledge about temporal intervals. Communications of the ACM 26, 832–843 (1983)
Badaloni, S., Giacomin, M.: A fuzzy extension of Allen’s interval algebra. In: Lamma, E., Mello, P. (eds.) AI*IA 1999. LNCS (LNAI), vol. 1792, pp. 155–165. Springer, Heidelberg (2000)
Badaloni, S., Giacomin, M.: Flexible temporal constraints. In: Proc. of IPMU 2000, Madrid, Spain, pp. 1262–1269 (2000)
Badaloni, S., Falda, M., Giacomin, M.: Integrating quantitative and qualitative constraints in fuzzy temporal networks. AI Communications 17, 183–272 (2004)
Badaloni, S., Giacomin, M.: Fuzzy extension of interval-based temporal sub-algebras. In: Proc. of IPMU 2002, Annecy, France, pp. 1119–1126 (2002)
Badaloni, S., Giacomin, M.: The algebra IAfuz: a framework for qualitative fuzzy temporal reasoning (2005) (to appear)
Barro, S., Marín, R., Mira, J., Paton, A.: A model and a language for the fuzzy representation and handling of time. Fuzzy Sets and Systems 175, 61–153 (1994)
Barro, S., Marín, R., Palacios, F., Ruiz, R.: Fuzzy logic in a patient supervision system. Artificial Intelligence in Medicine 21, 193–199 (2001)
Bouaud, J., Séroussi, B., Touzet, B.: Restoring the patient therapeutic history from prescription data to enable computerized guideline-based decision support in primary care. In: Proc. Medinfo 2004, pp. 120–124 (2004)
Dechter, R., Meiri, I., Pearl, J.: Temporal constraint networks. Artificial Intelligence 49, 61–95 (1991)
Dubois, D., Fargier, H., Prade, H.: Possibility theory in constraint satisfaction problems: Handling priority, preference and uncertainty. Applied Intelligence 6, 287–309 (1996)
Félix, P., Barro, S., Marín, R.: Fuzzy constraint networks for signal pattern recognition. Artificial Intelligence 148, 103–140 (2003)
Godo, L., Vila, L.: Possibilistic temporal reasoning based on fuzzy temporal constraints. In: Proc. of IJCAI 1995, pp. 1916–1922 (2001)
Keravnou, E.T.: Temporal constraints in clinical diagnosis. Journal of Intelligent and Fuzzy Systems 12, 49–67 (2002)
Mahfouf, M., Abbod, M.F., Linkens, D.A.: A survey of fuzzy logic monitoring and control utilization in medicine. Artificial Intelligence in Medicine 21, 27–42 (2001)
Marín, R., Cárdenas, M.A., Balsa, M., Sanchez, J.L.: Obtaining solutions in fuzzy constraint network. International Journal of Approximate Reasoning 16, 261–288 (1997)
Meiri, I.: Combining qualitative and quantitative constraints in temporal reasoning. Artificial Intelligence 87, 343–385 (1996)
Rossi, F., Sperduti, A., Venable, K.B., Khatib, L., Morris, P., Morris, R.: Learning and solving soft temporal constraints: An experimental study. In: Van Hentenryck, P. (ed.) CP 2002. LNCS, vol. 2470, p. 249. Springer, Heidelberg (2002)
Schwalb, E., Dechter, R.: Processing temporal constraint networks. Artificial Intelligence 93, 29–61 (1995)
Steimann, F.: On the use and usefulness of fuzzy sets in medical AI. Artificial Intelligence in Medicine 21, 131–137 (2001)
Wainer, J., Sandri, S.: Fuzzy temporal/categorical information in diagnosis. Journal of Intelligent Information Systems 11, 9–26 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Badaloni, S., Falda, M. (2005). Discriminating Exanthematic Diseases from Temporal Patterns of Patient Symptoms. In: Miksch, S., Hunter, J., Keravnou, E.T. (eds) Artificial Intelligence in Medicine. AIME 2005. Lecture Notes in Computer Science(), vol 3581. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527770_5
Download citation
DOI: https://doi.org/10.1007/11527770_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27831-3
Online ISBN: 978-3-540-31884-2
eBook Packages: Computer ScienceComputer Science (R0)