NasoNet, modeling the spread of nasopharyngeal cancer with networks of probabilistic events in discrete time.
The spread of cancer 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 that deals with both uncertainty and time. The ultimate goal is to know the stage of development of a cancer in a patient before selecting the appropriate treatment. A network of probabilistic events in discrete time (NPEDT) is a type of Bayesian network for temporal reasoning that models the causal mechanisms associated with the time evolution of a process. This paper describes NasoNet, a system that applies NPEDTs to the diagnosis and prognosis 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 general enough to be applied to any other type of cancer.
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Departamento de Inteligencia Artificial, Facultad de Ciencias de la UNED, Paseo senda del rey 9, Madrid 28040, Spain. seve@dia.uned.es
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D001185:Artificial Intelligence; D001499:Bayes Theorem; D018450:Disease Progression; D006801:Humans; D015233:Models, Statistical; D009303:Nasopharyngeal Neoplasms / Q000473:pathology*; D009361:Neoplasm Invasiveness; D009367:Neoplasm Staging; D011379:Prognosis