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

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2101, Issue , 2001, Pages 121-130

  Montani, Stefania ^a   Magni, Paolo ^a   Roudsari, Abdul V ^b   Carson, Ewart R ^b   Bellazzi, Riccardo ^a  

^a UNIVERSITY OF PAVIA   (Italy)

^b CITY UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION SUPPORT SYSTEMS; KNOWLEDGE MANAGEMENT; PATIENT TREATMENT;

CASE-BASED RETRIEVAL; CASEBASED REASONINGS (CBR); DECISION SUPPORTS; IMPLICIT KNOWLEDGE; MODEL-BASED REASONING; RULE BASED REASONING; TIGHT INTEGRATIONS; TYPE 1 DIABETES MELLITUS;

CASE BASED REASONING;

EID: 84947427662     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-48229-6_17     Document Type: Conference Paper

References (18)

1. Aha, D., and Daniels, J. (eds.): Proc. AAAI Workshop on CBR Integrations, AAAI Press (1998)
2. Kolodner, J.L.: Case-Based Reasoning. Morgan Kaufmann (1993)
3. Steels, L.: Corporate knowledge management. Proc. of ISMICK’93, Compiegne (1993) 9-30
4. Surma, J., Vanhoff, K.: Integrating rules and cases for the classification task. In: LNAI 1010, Proc. ICCBR, Springer Verlag (1995) 325-334
5. Xu, L.D.: An integrated rule- and case-based approach to AIDS initial assessment. International Journal of Biomedical Computing 40 (1996) 197-207
6. Branting, L.K., Porter, B.W.: Rules and precedents as complementary warrants. Proc. AAAI 91, Anaheim (1991)
7. Bichindaritz, I., Kansu, E., Sullivan, K.M.: Case-based reasoning in CARE- PARTNER: gathering evidence for evidence-based medical practice. In: LNAI 1488, Proc. 4th EWCBR, Springer Verlag (1998) 334-345
8. Leake, D.B.: Combining rules and cases to learn case adaptation. Proc. 17th International Conference of Cognitive Science Society, Pittsburgh (1995)
9. Sary, C., et al.: Trend analysis for spacecraft systems using multimodal reasoning. Proc. AAAI Spring Symp. on Multi-modal Reasoning, AAAI Press (1998) 157-162
10. Russel, S., Norvig, P.: Artificial Intelligence, a modern approach. Prentice Hall (1995)
11. Montani, S., Bellazzi, R., Portinale, L., Stefanelli, M.: A Multi-Modal Reasoning Methodology for Managing IDDM Patients. International Journal of Medical Informatics 58-59 (2000) 243-256
12. The Diabetes Control and Complication Trial Research Group: The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. The New England Journal of Medicine 329 (1993) 977-986
13. Deutsch, T., Roudsari, A.V., Leicester, H.J., Theodorou, T., Carson, E.R., and Sonksen, P.H.: UTOPIA: a consultation system for visit by visit diabetes management. Medical Informatics 21 (1996) 345-358
14. Andreassen, S., Benn, J., Hovorka, R., Olesen, K.G., Carson, E.R.: A probabilistic approach to glucose prediction and insulin dose adjustment: description of metabolic model and pilot evaluation study. Computer Methods and Programs in Biomedicine 41 (1994) 153-165
15. Ramoni, M., Sebastiani, P.: The use of exogenous knowledge to learn Bayesian Networks for incomplete databases. In: Liu, X., Cohen, P., and Berthold, M. (eds.): Advances In Intelligent Data Analysis, Springer Verlag, Berlin (1997) 537-548
16. Hovorka, R., Svacina, S., Carson, E.R., Williams, C.D., Sonksen, P.H.: A consultation system for insulin therapy. Computer Methods and Programs in Biomedicine 32 (1996) 303-310
17. Nucci, G., et al.: Verification phase final report, T-IDDM deliverable 5.2. http://aim.unipv.it/projects/tiddm/ftp.html
18. Cobelli, C., Nucci, G., Del Prato, S.: A physiological simulation model in type I Diabetes. Diabetes nutrition and Metabolism 11 (1998) 78