Abstract
Cancer spread is a non-deterministic dynamic process. As a consequence, the design of an assistant system for the diagnosis and prognosis of the extent of a cancer should be based on a representation method which deals with both uncertainty and time. The ultimate goal is to know the stage of development reached by a cancer in the patient, previously to selecting the appropriate treatment. A network of probabilistic events in discrete time (NPEDT) is a type of temporal Bayesian network that permits to model the causal mechanisms associated with the time evolution of a process. The present work describes NasoNet, a system which applies the formalism of NPEDTs to the case of nasopharyngeal cancer. We have made use of temporal noisy gates to model the dynamic causal interactions that take place in the domain. The methodology we describe is sufficiently general to be applied to any other type of cancer.
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Galán, S.F., Aguado, F., Díez, F.J., Mira, J. (2001). NasoNet, Joining Bayesian Networks, and Time to Model Nasopharyngeal Cancer Spread. In: Quaglini, S., Barahona, P., Andreassen, S. (eds) Artificial Intelligence in Medicine. AIME 2001. Lecture Notes in Computer Science(), vol 2101. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48229-6_30
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DOI: https://doi.org/10.1007/3-540-48229-6_30
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