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

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

doi:10.1007/3-540-48229-6_17

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

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

Risultato della ricerca: Capitolo in libro/report/atti di convegno › Contributo a conferenza › peer review

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.

Lingua originale	Inglese
Titolo della pubblicazione ospite	Artificial Intelligence in Medicine - 8th Conference on Artificial Intelligence in Medicine in Europe, AIME 2001, Proceedings
Editor	Silvana Quaglini, Pedro Barahona, Steen Andreassen
Editore	Springer Verlag
Pagine	121-130
Numero di pagine	10
ISBN (stampa)	3540422943, 9783540422945
DOI	https://doi.org/10.1007/3-540-48229-6_17
Stato di pubblicazione	Pubblicato - 2001
Pubblicato esternamente	Sì
Evento	8th Conference on Artificial Intelligence in Medicine in Europe, AIME 2001 - Cascais, Portugal Durata: 1 lug 2001 → 4 lug 2001

Serie di pubblicazioni

Nome	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2101
ISSN (stampa)	0302-9743
ISSN (elettronico)	1611-3349

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???event.eventtypes.event.conference???	8th Conference on Artificial Intelligence in Medicine in Europe, AIME 2001
Paese/Territorio	Portugal
Città	Cascais
Periodo	1/07/01 → 4/07/01

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10.1007/3-540-48229-6_17

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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 S. Quaglini, P. Barahona, & S. Andreassen (Edd.), Artificial Intelligence in Medicine - 8th Conference on Artificial Intelligence in Medicine in Europe, AIME 2001, Proceedings (pagg. 121-130). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2101). Springer Verlag. https://doi.org/10.1007/3-540-48229-6_17

Montani, Stefania ; Magni, Paolo ; Roudsari, Abdul V. et al. / Integrating different methodologies for insulin therapy support in type 1 diabetic patients. Artificial Intelligence in Medicine - 8th Conference on Artificial Intelligence in Medicine in Europe, AIME 2001, Proceedings. curatore / Silvana Quaglini ; Pedro Barahona ; Steen Andreassen. Springer Verlag, 2001. pagg. 121-130 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Integrating different methodologies for insulin therapy support in type 1 diabetic patients",

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{\textquoteright}s needs, overcoming the single approaches{\textquoteright} 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{\textquoteright} data.",

author = "Stefania Montani and Paolo Magni and Roudsari, {Abdul V.} and Carson, {Ewart R.} and Riccardo Bellazzi",

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doi = "10.1007/3-540-48229-6_17",

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Montani, S, Magni, P, Roudsari, AV, Carson, ER & Bellazzi, R 2001, Integrating different methodologies for insulin therapy support in type 1 diabetic patients. in S Quaglini, P Barahona & S Andreassen (edd.), Artificial Intelligence in Medicine - 8th Conference on Artificial Intelligence in Medicine in Europe, AIME 2001, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2101, Springer Verlag, pagg. 121-130, 8th Conference on Artificial Intelligence in Medicine in Europe, AIME 2001, Cascais, Portugal, 1/07/01. https://doi.org/10.1007/3-540-48229-6_17

Integrating different methodologies for insulin therapy support in type 1 diabetic patients. / Montani, Stefania; Magni, Paolo; Roudsari, Abdul V. et al.
Artificial Intelligence in Medicine - 8th Conference on Artificial Intelligence in Medicine in Europe, AIME 2001, Proceedings. a cura di / Silvana Quaglini; Pedro Barahona; Steen Andreassen. Springer Verlag, 2001. pag. 121-130 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2101).

Risultato della ricerca: Capitolo in libro/report/atti di convegno › Contributo a conferenza › peer review

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T1 - Integrating different methodologies for insulin therapy support in type 1 diabetic patients

AU - Montani, Stefania

AU - Magni, Paolo

AU - Roudsari, Abdul V.

AU - Carson, Ewart R.

AU - Bellazzi, Riccardo

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2001.

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N2 - 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.

AB - 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.

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ER -

Montani S, Magni P, Roudsari AV, Carson ER, Bellazzi R. Integrating different methodologies for insulin therapy support in type 1 diabetic patients. In Quaglini S, Barahona P, Andreassen S, editor, Artificial Intelligence in Medicine - 8th Conference on Artificial Intelligence in Medicine in Europe, AIME 2001, Proceedings. Springer Verlag. 2001. pag. 121-130. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-48229-6_17

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

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