Artificial intelligence techniques for diabetes management: the T-IDDM project

Stefania MONTANI, BELLAZZI R., RIVA A., LARIZZA C., Luigi PORTINALE, STEFANELLI M.

Risultato della ricerca: Contributo alla conferenzaContributo in Atti di Convegnopeer review

Abstract

We present a successful application of Artificial Intelligence (AI) methodologies in the context of a telemedicine service for diabetic patients management, developed within the EU-funded T-IDDM project. The system architecture is distributed, and composed by a Patient Unit and by a Medical Unit, connected through a telecommunication link. Several AI methods have been exploited to implement the T-IDDM functionality. The data base relies on an explicit representation of the domain ontology. Temporal Abstractions and other Intelligent Data Analysis techniques are used to analyse the patient’s monitoring data; the Case Based Reasoning (CBR) methodology is applied to perform the Knowledge Management task. Finally, CBR is integrated with Rule Based Reasoning to provide physicians with a multi-modal reasoning decision support tool. The T-IDDM service is being tested through a small on field trial in Pavia; the first results, though preliminary, seem to substantiate the hypothesis that the use of an AI-based telemedicine system could present an advantage in the management of type 1 diabetic patients, leading to a more tight control of the patients’ metabolic situation, in a cost-effective way.
Lingua originaleInglese
Pagine716-720
Numero di pagine5
Stato di pubblicazionePubblicato - 1 gen 2000
EventoPAIS 2000, Prestigious Applications of Intelligent Systems / European Conference on Artificial Intelligence (ECAI -PAIS) ??? - Berlin, Germany
Durata: 1 gen 2000 → …

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???event.eventtypes.event.conference???PAIS 2000, Prestigious Applications of Intelligent Systems / European Conference on Artificial Intelligence (ECAI -PAIS) ???
CittàBerlin, Germany
Periodo1/01/00 → …

Keywords

  • Knowledge Representation
  • Medical Informatics

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