專家系統自1965年發展至今，其發展是與日遽增，在邁入二十一世紀這個新紀元，傳統的專家系統遭遇到不同以往的問題，不僅處理的問題複雜度提高之外，在建置系統的過程中需要更多專家提供其寶貴的意見，以期讓系統在處理問題的層面能更加寬廣及增進其彈性和效用。因此，多專家系統能夠解決傳統上單一專家先天上的限制。Gaines和Shaw於1989年在其論著中指出，利用一群專家的知識來發展專家系統其效益比單一專家來的更好。 然而，在多專家的專家系統中會產生案例選取的衝突，過去大都依賴人為的經驗法則判斷。如此一來，不僅在處理的時間成本上產生耗損，其選取案例的公信力亦容易令人產生存疑。此外在相關的研究上，絕大多數都未對於此一衝突提出另外一套較具公正性的解決辦法。 針對此一現象，本研究發現欲解決其中之案例衝突，可以藉由群體決策和多評準決策領域中尋求解決之辦法，透過本研究一連串的文獻蒐集與探討，得到Nemawashi決策模式可以加以導入應用，因此，本研究嘗試引用案例式推論（Case-Base Reasoning）、Nemawashi 決策模式，提出一個整合多專家的意見和解決其案例產生衝突的方法。 / Expert system has been in speeding development since 1965. With the advent of the 21st century, the traditional expert system is encountering problems different from the past. With the rising complexity of nowadays problems, it requires valuable opinions from more professionals in the construction of expert system. The multi-expert knowledge can not only broaden the scope in which the system handles problems, but also enhance the system’s flexibility and efficiency. Thus, multi-expert system outsmarts the conventional expert system which is restricted by the voice of a single expert. Gaines and Shaw in 1989 commented that the expert system was better quipped with a group of experts than with one single expert. Nevertheless, multi-expert system contains the problem of case conflict. To undermine the conflict, it is common for people to resort to experts’ judgments and their personal experiences. Consequently, the multi-expert system has the disadvantage of consuming time in the process of case selection. Moreover, the case selected out of this process may be unconvincing due to its overdependence on human decisions. As to the problem of case conflict, most of the researches related to multi-expert system do not propose other more objective solutions. Focus on the problem mentioned above, this study tends to solve case conflict through the use of Group Decision and Multiple Criteria Decision-Making (MCDM). After the collection and analysis of data, the study finds out that Nemawashi Decisions are effective in handling the problem of case conflict. Thus, this study attempts to apply Nemawashi Decisions in Case-Base Reasoning in order to combine opinions from different experts and to solve the case conflict in the multi-expert system.

Made available in DSpace on 2015-05-14T12:36:36Z (GMT). No. of bitstreams: 1 ArquivoTotalRenata.pdf: 2638998 bytes, checksum: c2e2373b63cd157ac4705018a4e58468 (MD5) Previous issue date: 2013-02-19 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / In recent years, there was an increasing interest in the use of information technology in the medical field. In this way, several studies have been conducted regarding a large range of diseases, including cancer. For example, there are organizations that maintain databases, which record information about cases of cancer around the world, so that health personnel can investigate such bases and find possible classifications for an initial diagnostic according to the symptoms presented by their patients. However, these databases do not offer a proper support for this investigation. This dissertation discusses the use of the of case-based reasoning and rule-based reasoning technologies as a solution to support medical diagnosis via representations of actual patients and adaptations of these cases to define more specialized diagnoses, according to the peculiarities of each patient. The data collection was carried out in the Napoleao Laureano Hospital, at Joao Pessoa city, and the information inherent in the structure of the case, the rules and weights were defined in accordance with the specialized literature and conversations with health professionals. Despite the absence of a specialist, the small base of cases and presence of limited information on each of them, the results showed that the system is effective as a RBC system aimed at diagnosis about cancer. The focus of this project is on the main gastrointestinal cancer domain, but the ideas can be extended to other fields of cancer. / O tratamento de pacientes com câncer é um desafio para os hospitais e centros de saúde. O primeiro problema é classificar ou identificar o tipo específico de câncer. Em seguida, os médicos devem determinar um tratamento adequado para tal doença. Existem bancos de dados mundiais que registram informações sobre câncer, de modo que os médicos possam tentar achar uma classificação para a doença de acordo com os sintomas apresentados pelo paciente. Contudo eles não oferecem um bom suporte para a procura das doenças e, principalmente, para a identificação do tratamento. Este artigo propõe a utilização das tecnologias de raciocínio baseado em casos e raciocínio baseado em regras como solução para suportar estas duas atividades via representações de casos de pacientes e adaptações destes casos para a definição de diagnósticos mais especializados, de acordo com as peculiaridades de cada paciente. Nosso foco é sobre o câncer gastrointestinal, mas as idéias podem ser estendidas para outros domínios de câncer.

Le cancer du sein est le cancer le plus répandu chez les femmes de plus de 40 ans. En effet, des études ont montré qu'une détection précoce et un traitement approprié du cancer du sein augmentent de manière significative les chances de survie. La mammographie constitue le moyen d'investigation le plus utilisé dans le diagnostic des lésions mammaires. Cependant, cette technique peut être insuffisante pour montrer les structures du sein et faire apparaître les anomalies présentes et le médecin peut faire appel à d'autres modalités d'imagerie telle que l'imagerie IRM. Ces modalités sont généralement complémentaires. Par conséquent, le médecin procède à une fusion mentale des différentes informations sur les deux images dans le but d'effectuer le diagnostic adéquat. Pour assister le médecin et l'aider dans ce processus, nous proposons une solution permettant de fusionner les deux images. Bien que l'idée de la fusion paraisse simple, sa mise en oeuvre pose de nombreux problèmes liés non seulement au problème de fusion en général mais aussi à la nature des images médicales qui sont généralement des images mal contrastées et présentant des données hétérogènes, imprécises et ambigües. Notons que les images mammographiques et les images IRM présentent des représentations très différentes des informations, étant donnée qu'elles sont prises dans des conditions distinctes. Ce qui nous amène à poser la question suivante: Comment passer de la représentation hétérogène des informations dans l'espace image, à un autre espace de représentation uniforme. Afin de traiter cette problématique, nous optons pour une approche de traitement multi-niveaux : niveau pixel, niveau primitives, niveau objet et niveau scène. Nous modélisons les objets pathologiques extraits des différentes images par des ontologies locales. La fusion est ensuite effectuée sur ces ontologies locales et résulte en une ontologie globale contenant les différentes connaissances sur les objets pathologiques du cas étudié. Cette ontologie globale sert à instancier une ontologie de référence modélisant les connaissances du diagnostic médical des lésions mammaires. Un raisonnement à base de cas est exploité pour fournir les rapports diagnostic des cas les plus similaires pouvant aider le médecin à prendre la meilleure décision. Dans le but de modéliser l'imperfection des informations traitées, nous utilisons la théorie des possibilités avec les différentes ontologies. Le résultat fourni est présenté sous forme de rapports diagnostic comportant les cas les plus similaires au cas étudié avec des degrés de similarité exprimés en mesures de possibilité. Un modèle virtuel 3D complète le rapport diagnostic par un aperçu simplifié de la scène étudiée. / The breast cancer is the most prevalent cancer among women over 40 years old. Indeed, studies evinced that an early detection and an appropriate treatment of breast cancer increases significantly the chances of survival. The mammography is the most tool used in the diagnosis of breast lesions. However, this technique may be insufficient to evince the structures of the breast and reveal the anomalies present. The doctor can use additional imaging modalities such as MRI (Magnetic Reasoning Image). Therefore, the doctor proceeds to a mental fusion of the different information on the two images in order to make the adequate diagnosis. To assist the doctor in this process, we propose a solution to merge the two images. Although the idea of the fusion seems simple, its implementation poses many problems not only related to the paradigm of fusion in general but also to the nature of medical images that are generally poorly contrasted images, and presenting heterogeneous, inaccurate and ambiguous data. Mammography images and IRM images present very different information representations, since they are taken under different conditions. Which leads us to pose the following question: How to pass from the heterogeneous representation of information in the image space, to another space of uniform representation from the two modalities? In order to treat this problem, we opt a multilevel processing approach : the pixel level, the primitive level, the object level and the scene level. We model the pathological objects extracted from the different images by local ontologies. The fusion is then performed on these local ontologies and results in a global ontology containing the different knowledge on the pathological objects of the studied case. This global ontology serves to instantiate a reference ontology modeling knowledge of the medical diagnosis of breast lesions. Case-based reasoning (CBR) is used to provide the diagnostic reports of the most similar cases that can help the doctor to make the best decision. In order to model the imperfection of the treated information, we use the possibility theory with the ontologies. The final result is a diagnostic reports containing the most similar cases to the studied case with similarity degrees expressed with possibility measures. A 3D symbolic model complete the diagnostic report with a simplified overview of the studied scene.