Integrating different methodologies for insulin therapy support in type 1 diabetic patients

Stefania Montani, Paolo Magni, Abdul V. Roudsari, Ewart R. Carson, Riccardo Bellazzi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a Multi Modal Reasoning (MMR) methodology designed to provide physicians with knowledge management and decision support functionality in the context of type 1 diabetes mellitus care. The MMR system performs a tight integration of Case Based Reasoning (CBR), Rule Based Reasoning (RBR) and Model Based Reasoning (MBR), with the aim of suggesting a therapy properly tailored to the patient’s needs, overcoming the single approaches’ limitations. This methodology allows the exploitation of the implicit knowledge embedded in patients' visits (past cases) and in monitoring data through Case Based retrieval. Moreover the explicit domain knowledge is formalized in a set of production rules and in a mathematical model. The system has been preliminary tested both on simulated and on real patients’ data.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages121-130
Number of pages10
Volume2101
ISBN (Print)3540422943, 9783540422945
Publication statusPublished - 2001
Event8th Conference on Artificial Intelligence in Medicine in Europe, AIME 2001 - Cascais, Portugal
Duration: Jul 1 2001Jul 4 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2101
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other8th Conference on Artificial Intelligence in Medicine in Europe, AIME 2001
CountryPortugal
CityCascais
Period7/1/017/4/01

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Fingerprint Dive into the research topics of 'Integrating different methodologies for insulin therapy support in type 1 diabetic patients'. Together they form a unique fingerprint.

  • Cite this

    Montani, S., Magni, P., Roudsari, A. V., Carson, E. R., & Bellazzi, R. (2001). Integrating different methodologies for insulin therapy support in type 1 diabetic patients. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2101, pp. 121-130). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2101). Springer Verlag.