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Methods Inf Med 2001; 40(01): 46-51
DOI: 10.1055/s-0038-1634463
Original Article
Schattauer GmbH

Individual Prognosis of Diabetes Long-term Risks: A CBR Approach

E. Armengol
1   Artificial Intelligence Research Institute (IIIA-CSIC), Campus UAB, Bellaterra, Catalonia, Spain
,
A. Palaudàries
2   Unitat d’Endocrinologia, Hospital de Mataró, Carretera de Cirera, Mataró, Catalonia, Spain
,
E. Plaza
1   Artificial Intelligence Research Institute (IIIA-CSIC), Campus UAB, Bellaterra, Catalonia, Spain
› Author Affiliations
Further Information

Publication History

Publication Date:
08 February 2018 (online)

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Abstract

We present DIRAS, an application to support physicians in determining the risk of complications for individual diabetic patients. The risk pattern of each diabetic patient is obtained using a Case-based Reasoning method called LID. Case-based Reasoning is an Artificial Intelligence technique based on solving new situations according to past experiences. For each patient, the LID method determines the risk of each diabetic complication according to the risk of already diagnosed patients. In addition, LID builds a description that can be viewed as an explanation of the obtained risk.

Keywords

Diabetes Mellitus - Individual Prognosis - Artificial Intelligence - Case-based Reasoning
 

  • References

  • 1 Lucas PJF, Abu-Hanna A. Prognostic methods in medicine. Artif Intell Med 1999; 15: 105-9.
    CrossrefPubMedGoogle Scholar
  • 2 Kolodner JL. Case-based Reasoning. Morgan Kauffman; 1993
    Google Scholar
  • 3 Armengol E, Plaza E. A knowledge level model of Case-based Reasoning. In: Topics in Case-based Reasoning. Lecture Notes in Artificial Intelligence, 837. Wess S, Althoff KD, Richter MM. Eds 1994: 53-64.
    Google Scholar
  • 4 Armengol E. A framework for integrated learning and problem solving. Monografies de l’IIIA. Vol 5. Institut d’Investigació en Intel. ligència Artificial, Ed. Barcelona: 1997. http://www.iiia.csic.es/Publications/monographs.html.
    Google Scholar
  • 5 Armengol E, Plaza E. Bottom-up induction of feature terms. Machine Learning Journal 2000; 41: 259-94.
    CrossrefPubMedGoogle Scholar
  • 6 López de Mántaras R. A Distance-based Attribute Selection Measure for Decision Tree Induction. Machine Learning 1991; 6: 81-92.
    CrossrefPubMedGoogle Scholar
  • 7 Quinlan JR. Induction of decision trees. Machine Learning 1986; 1: 81-106.
    PubMedGoogle Scholar
  • 8 Verdaguer A, Patak A, Sancho JJ, Sierra C, Sanz F. Validation of the Medical System PNEUMON-IA. In: Yearbook of Medical Informatics 1993. van Bemmel JH, McCray AT. eds. IMIA’s Publications. IMIA’s Publications; 1993-446.
    Google Scholar
  • 9 Lehmann EE, Deutsch T, Carson ER, Sönksen PH. Combining rule-based resoning and mathematical modelling in diabetes care. Artif Intell Med 1994; 6: 137-60.
    CrossrefPubMedGoogle Scholar
  • 10 Larizza C, Bellazzi R, Riva A. Temporal abstractions for diabetic patients management. Proceedings of AIME 97. Keravnou E, Garbay C, Baud R, Wyatt J. eds. 1997: 319-30.
    PubMedGoogle Scholar