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RESEARCH ARTICLE   Open Access    

Decentralized case-based reasoning and Semantic Web technologies applied to decision support in oncology

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  • Abstract: This article presents the Kasimir system dedicated to decision knowledge management in oncology and which is built on top of Semantic Web technologies, taking benefit from standard knowledge representation formalisms and open reasoning tools. The representation of medical decision protocols, in particular for breast cancer treatment, is based on concepts and instances implemented within the description logic OWL DL (Web ontology language description logic). The knowledge units related to a protocol can then be applied for solving specific medical problems, using instance or concept classification. However, the straight application of a protocol is not always satisfactory, for example, because of contraindications, necessitating an adaptation of the protocol. This is why the principles and methods of case-based reasoning (CBR) in the framework of DLs have been used. In addition, the domain of oncology is complex and involves several specialties, for example, surgery and chemotherapy. This complexity can be better undertaken with a viewpoint-based representation of protocols and viewpoint-based reasoning, for either application or adaptation of the protocols. Accordingly, a distributed DL has been used for representing a viewpoint-based protocol. The application and the adaptation of the viewpoint-based protocol to medical cases is carried out using global instance classification and decentralized CBR.
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  • Cite this article

    Mathieu d'Aquin, Jean Lieber, Amedeo Napoli. 2013. Decentralized case-based reasoning and Semantic Web technologies applied to decision support in oncology. The Knowledge Engineering Review 28(4)425−449, doi: 10.1017/S0269888913000027
    Mathieu d'Aquin, Jean Lieber, Amedeo Napoli. 2013. Decentralized case-based reasoning and Semantic Web technologies applied to decision support in oncology. The Knowledge Engineering Review 28(4)425−449, doi: 10.1017/S0269888913000027

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RESEARCH ARTICLE   Open Access    

Decentralized case-based reasoning and Semantic Web technologies applied to decision support in oncology

The Knowledge Engineering Review  28 2013, 28(4): 425−449  |  Cite this article

Abstract: Abstract: This article presents the Kasimir system dedicated to decision knowledge management in oncology and which is built on top of Semantic Web technologies, taking benefit from standard knowledge representation formalisms and open reasoning tools. The representation of medical decision protocols, in particular for breast cancer treatment, is based on concepts and instances implemented within the description logic OWL DL (Web ontology language description logic). The knowledge units related to a protocol can then be applied for solving specific medical problems, using instance or concept classification. However, the straight application of a protocol is not always satisfactory, for example, because of contraindications, necessitating an adaptation of the protocol. This is why the principles and methods of case-based reasoning (CBR) in the framework of DLs have been used. In addition, the domain of oncology is complex and involves several specialties, for example, surgery and chemotherapy. This complexity can be better undertaken with a viewpoint-based representation of protocols and viewpoint-based reasoning, for either application or adaptation of the protocols. Accordingly, a distributed DL has been used for representing a viewpoint-based protocol. The application and the adaptation of the viewpoint-based protocol to medical cases is carried out using global instance classification and decentralized CBR.

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    Cite this article
    Mathieu d'Aquin, Jean Lieber, Amedeo Napoli. 2013. Decentralized case-based reasoning and Semantic Web technologies applied to decision support in oncology. The Knowledge Engineering Review 28(4)425−449, doi: 10.1017/S0269888913000027
    Mathieu d'Aquin, Jean Lieber, Amedeo Napoli. 2013. Decentralized case-based reasoning and Semantic Web technologies applied to decision support in oncology. The Knowledge Engineering Review 28(4)425−449, doi: 10.1017/S0269888913000027
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