Large quantities of sensory data may be generated within a pervasive medical environment. Using the information gathered, intelligent systems may be developed to assist our medical practitioners in real-time. Patient datasets require constant scrutiny and must be analysed in the context of other available information (e.g. patient profile, medical knowledge base). An accurate real-time overview of a patients state of health is not always possible as communication links may break or servers fail. Therefore it is essential that the information provided on a patients state of health in taken in the context of available datasets. Presented is a Data Management System-Tripartite Ontology Medical Reasoning Model (DMS-TOMRM). It is built on three input streams 1) External stimuli (e.g. patient vital signs, patient location), 2) Medical knowledge base (medical database, ontologies) and 3) User profiles (medical history and patient properties). All three pools of information are merged together to provide the medical practitioner with a real-time diagnosis assistant. A key element of the DMS-TOMRM is its ability to cope with physical failures. For example, if the medical knowledge base fails, the DMS-TOMRM may still provide a diagnosis based on the users profile and current real-time sensor values. This supports the DMS principle of providing a higher quality of service at the patient point of care. Presented is the DMS-TOMRM and how it intelligently interacts with a context rich medical environment
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