NasoNet, modeling the spread of nasopharyngeal cancer with networks of probabilistic events in discrete time

Abstract

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.

Impact of precision of Bayesian network parameters on accuracy of medical diagnostic systems

“…Early work in this direction was done by Leong and colleagues e.g., [21, 22]. Selected applications of DBNs in medicine included also a DBN for management of patients suffering from a carcinoid tumor [23], or NasoNet, a system for diagnosis and prognosis of nasopharyngeal cancer [24]. DBNs were also used in cellular systems [25] or for modeling dynamics of organ failure in patients in intensive care units [26].…”

A knowledge-based approach to assign breast cancer treatments in oncology units

“…The knowledge-based system intends to facilitate oncologists the maintenance of the knowledge base, thus guaranteeing that its content is updated and efficient decisions can be made. Some decision support systems (DSS) based on artificial intelligent techniques such as neural networks (Zhou et al, 2002), probabilistic events networks (Galán, Aguado, Diéz & Mira, 2002) or hybrid systems (Papadopoulos, Fotiadisb & Likas, 2002) have been put into oncology clinical practice. The main advantage of our knowledge-based system proposed in this paper with respect with those is the use of technologies that facilitate an easy maintenance of the knowledge base.…”

Clinical Decision Support with Guidelines and Bayesian Networks

“…CardioBayes was designed as a learning environment for students and is based on a Bayesian network design by domain experts. One example for prognostic reasoning is NasoNet (Galan et al, 2002), which extends a Bayesian network model with temporal reasoning. TREAT, a system for treatment selection, has already been introduced in section 2.2.…”