2011 IEEE International Conference on Fuzzy Systems June 27-30, 2011, Taipei, Taiwan 978-1-4244-7317-5/11/$26.00 ©2011 IEEE Fuzzy Knowledge Approach to Automatic Disease Diagnosis Carmen De Maio, Vincenzo Loia Dipartimento di Informatica Università degli Studi di Salerno Fisciano (SA), Italy {cdemaio, loia}@unisa.it Giuseppe Fenza, Mariacristina Gallo CORISA Università degli Studi di Salerno Fisciano (SA), Italy {gfenza, mcgallo}@corisa.it Roberto Linciano, Aldo Morrone Azienda Ospedaliera San Camillo- Forlanini Roma, Italy rlinciano@yahoo.it, amorrone@scamilloforlanini.rm.it Abstract— Applying best available evidences to clinical decision making requires medical research sharing and (re)using. Recently, computer assisted medical decision making is taking advantage of Semantic Web technologies. In particular, the power of ontologies allows to share medical research and to provide suitable support to the physician’s practices. This paper describes a system, named ODINO (Ontological DIsease kNOwledge), aimed at supporting medical decision making through semantic based modeling of medical knowledge base. The system defines an ontology model able to represent relations between medical disease and its symptomatology in a qualitative manner by using fuzzy labels. Medical knowledge is defined according with physician experts members of INMP 1 (National Institute for Health Migration and Poverty). The main aim of ODINO is to provide an effective user interface by using ontologies and controlled vocabularies and by allowing faceted search of diseases. In particular, this work mashes the capabilities of Description Logic reasoners and information retrieval techniques in order to answer to physician’s requests. Some experimental results are given in the field of dermatological diseases. Keywords- Semantic Web, Ontology, Clinical DSS, Diagnosis I. INTRODUCTION Traditional approaches to the medical diagnosis practice have many drawbacks, like as: the huge growth of biomedical information has made difficult the retraining for the individual doctors; the poor dissemination of effective research results; and so on. New trend is the Evidence-Based Medicine (EBM) which aims to apply the best available evidences gained from the scientific methods to clinical decision making. The challenge of EBM is to define a systematic approach to integrate research results with clinical expertise and patient preferences, and exploit them during the medical diagnosis. So, it is necessary to provide a suitable model to structure medical results and to perform the knowledge spreading and sharing. There are several ongoing efforts aimed at developing formal models of medical knowledge and reasoning to design decision support systems. These efforts have focused on representing content of clinical guidelines and their logical structure. Semantic Web technologies and ontologies are enabling elements to achieve these aims. In fact, today 1 http://www.inmp.it/ ontologies are assuming increasingly important role in the area of knowledge based decision support systems by introducing capabilities in terms of logic based reasoning. This work presents ODINO, a multilingual web based application, that addresses the aim to use semantic web technologies in order to support medical practices through an effective user interface. In particular, ontologies are used to model available medical diseases features (e.g., skin diseases), symptomatologies, treatment protocols and so on. The relations between symptomatologies (i.e., symptoms and signs) and available diseases are represented by using fuzzy labels resulting from the analysis of medical expertise included in [1]. Standard formalisms, like OWL and SKOS, are used to specify domain knowledge and controlled vocabularies, i.e., diseases, symptomatology, active ingredients and clinical tests according to standard specifications, like as ICD-9-CM 2 . Ontologies, controlled vocabularies and information retrieval techniques are exploited to provide typical capabilities of Semantic Web portals [2] and medical decision support. Some of the main features of the system are: • disease catalogue browsing including images, symptoms and signs, treatments, etc. to support rapid training; • preliminary medical diagnosis, indeed medical knowledge querying by specifying symptoms, signs and complications, to find eligible diseases; • faceted search of diseases by enabling multi-criteria selections (i.e., symptomatologies, complications, active ingredients, etc.) to support differential diagnosis. ODINO results are explained to physician by highlighting symptoms/signs/complications that match the retrieved diagnosis and by suggesting other important features of founded diseases. The paper is organized as follows. Section II introduce some related works in the applicative domain. Section III describe the knowledge layer of ODINO. The medical decision support methodology is described in Section IV. Then, Section V details features of system and provides the results of the case 2 International Statistical Classification of Diseases, 9th Revision, Clinical Modification 2088