Case-based reasoning
Machine Learning
Integration Rules and Cases for the Classification Task
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Selecting Most Adaptable Diagnostic Solutions through Pivoting-Based Retrieval
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
The Use of Exogenous Knowledge to Learn Bayesian Networks from Incomplete Databases
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
Temporal Abstractions for Diabetic Patients Management
AIME '97 Proceedings of the 6th Conference on Artificial Intelligence in Medicine in Europe
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
An ontology for computer-based decision support in rehabilitation
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
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We present a decision support tool for Insulin Dependent Diabetes Mellitus management, that relies on the integration of two different methodologies: Rule-Based Reasoning (RBR) and Case-Based Reasoning (CBR). This multi-modal reasoning system aims at providing physicians with a suitable solution to the problem of therapy planning by exploiting the strengths of the two selected methods. RBR provides suggestions on the basis of a situation detection mechanism that relies on structured prior knowledge; CBR is used to specialize and dynamically adapt the rules on the basis of the patient's characteristics and of the accumulated experience. Such work will be integrated in the EU funded project T-IDDM architecture, and has been preliminary tested on a set of cases generated by a diabetic patient simulator.