Integrating Healthcare Knowledge Artifacts for Clinical Decision Support: Towards Semantic Web Based Healthcare Knowledge Morphing
ABSTRACT Healthcare decision making demands the systematic integration of knowledge from multiple sources, such as clinical guidelines,
clinical pathways, knowledge of practitioners and so on. We present a semantic web based approach for synthesizing health
knowledge through the semantic modeling of healthcare knowledge as ontologies and reasoning over the ontologies to derive
a morphed knowledge object. We demonstrate the application of our approach by generating morphed knowledge about prostate
cancer clinical pathways.
SourceAvailable from: Jos De Roo
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ABSTRACT: A flexible support system to offer warnings, therapies and recommendations for remote patient surveillance is presented. This system aims to improve the home healthcare assistance of patients with acute, light and medium-severe forms of diseases, which were previously diagnosed by family doctors (general practitioners) and specialists. It is used to early detect possible symptoms and signs of complications and also to adapt the treatment and to increase its efficiency. Symptoms and personal reactivity to prescribed medications (specific response to treatments) can be better monitored and interpreted. An inference mechanism based on Petri nets and Fuzzy theory was designed and experimentally implemented for diseases evolution supervising purposes. The doctor — patients scheduled or emergence communication sessions are based on Internet and mobile telephony technologies.
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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.The Knowledge Engineering Review 12/2013; 28(04). DOI:10.1017/S0269888913000027 · 0.96 Impact Factor