Medicine expert system dynamic Bayesian Network and ontology based Octavian Arsene ⇑ , Ioan Dumitrache, Ioana Mihu Laboratory of Intelligent Systems, ‘‘Politehnica’’ University of Bucharest, SPl. Independentei 313, 060042 Bucharest, Romania article info Keywords: Expert system Ontology Bayesian Network abstract The paper proposes an application framework to be used for medicine assisted diagnosis based on ontol- ogy and Bayesian Network (DBNO). There are two goals: (1) to separate the domain knowledge from the probabilistic information and (2) to create an intuitive user interface. The framework architecture has three layers: knowledge, uncertainty model and user interface. The contributions of the domain experts are decoupled, the ontology builder will create the domain concepts and relationships focusing on the domain knowledge only. The uncertainty model is Bayesian Network and the probabilities of the vari- ables states are stored in a profile repository. The diagnostician will use the user interface feeded with the domain ontology and one uncertainty profile. The application was tested on a sample medicine model for the diagnose of heart disease. Ó 2011 Elsevier Ltd. All rights reserved. 1. Introduction The diagnosis can be defined as the process of identifying a set of hypotheses that model the problem domain and finding that one with highest probability of matching the real world state. In med- ical diagnosis, the uncertainty arises from the inability to evaluate the degree of truth of a hypothesis due to unreliable and incom- plete information or inconsistent knowledge. The ontology and Bayesian Network (BN) methodologies have been chosen to address knowledge management and uncertainty. The ontology enables the representation of a domain knowledge in a machine understandable form. It can represent the organiza- tional structure of a large complex domains, but the inability to deal with the uncertainty can be a drawback for its application. One disadvantage of the BN is representation of complex struc- tured domains point of view. The ontology and BN can comple- ment themselves in order to overcome the each other disadvantages, thus an ontology-driven uncertainty model can be created. The main goal of the paper is to propose an application frame- work as a collaborative expert system for creating, developing and maintaining a general model for medicine assisted diagnosis (Fig. 1). From user point of view there will be three roles based on their competencies: concepts and relations definition, connect probabilities to states of the concepts, setting evidences in order to assist the diagnosis. Each role is assigned to one of the triangle’ sides and thus depicting three operational layers. The automated connection between all three layers increases the efficiency of the entire process. The proposed model is implemented as a soft- ware using PROTEGE as an ontology framework, NETICA API (Application Programming Interface) as a Bayesian API and Java technology as a development platform. The mapping between domain knowledge and uncertainty model is based on the fact that each concept defined into ontology is part of the BN as a variable. The diagnostician will use an intuitive graphical user interface for changing evidences of the BN variables and based on a thresh- old the application will depict a chart having the most significant nodes. The final chart will assist the medicine diagnostician to identify the significant factors for a particular case. In order to offer more flexibility the domain knowledge and probabilities are already build and ready to be used as a medicine ontology and respectively uncertainty profile. There is no need to be re-created each time during diagnosis phase. The diagnostician can choose between more than one uncertainty profiles for a medicine ontol- ogy. The relation between ontology and uncertainty profile is one to many (in case there are several sources for probabilities tables for the same ontology). This approach speed up the entire diagno- sis process and allows the asynchronous update of the ontology and uncertainty profiles by the domain experts in order to increase the degree of accuracy of the information. A similar model, OntoBayes, was proposed in Yi (2007). The major difference between OntoBayes and this proposed model resides in separation between domain knowledge and quantitative component of BN in order to decouple the ontology from the uncertainty probabilities. BayesOWL (Zhongli, 2005) and PR-OWL (Cesar, Costa, Laskey, & Laskey, 2003) are others probabilistic ontology approaches facilitating ontology mapping in the semantic web. Some of their limitations refer to: two-valued variables only 0957-4174/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2011.05.074 ⇑ Corresponding author. E-mail address: octavianarsene@gmail.com (O. Arsene). Expert Systems with Applications 38 (2011) 15253–15261 Contents lists available at ScienceDirect Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa