Case-based reasoning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
A Hybrid Knowledge-Based System for Technical Diagnosis Learning and Assistance
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
Retrieval in a Prototype-Based Case Library: A Case Study in Diabetes Therapy Revision
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Experiences with Prototype Designs and Retrieval Methods in Medical Case-Based Reasoning Systems
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
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
Integrating case-based and rule based reasoning: the possibilistic connection
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
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
Case-Based Reasoning in CARE-PARTNER: Gathering Evidence for Evidence-Based Medical Practice
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Combining case-based and rule-based reasoning: a heuristic approach
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Evaluating a Multi-modal Reasoning Sytem in Diabetes Care
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Case-Based Reasoning in the Care of Alzheimer's Disease Patients
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
The Role of Information Extraction for Textual CBR
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Cost-sensitive case-based reasoning using a genetic algorithm: Application to medical diagnosis
Artificial Intelligence in Medicine
The 4 diabetes support system: a case study in CBR research and development
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
Performance index assessment of intelligent computing methods in EMG-based neuromuscular diseases
International Journal of Knowledge Engineering and Soft Data Paradigms
Expert Systems with Applications: An International Journal
Developing a hybrid predictive system for retinopathy
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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The integration of rule-based and case-based reasoning is particularly useful in medical applications, where both general rules and specific patient cases are usually available. In the present paper we aim at presenting a decision support tool for Insulin Dependent Diabetes Mellitus management relying on such a kind of integration. This multimodal reasoning system aims at providing physicians with a suitable solution to the problem of therapy planning by exploiting, in the most exible way, the strengths of the two selected methods. In particular, the integration is pursued without considering one of the modality as the most prominent reasoning method, but exploiting complementarity in all possible ways. In fact, while rules provide suggestions on the basis of a situation detection mechanism that relies on structured prior knowledge, CBR may be used to specialize and dynamically adapt the rules on the basis of the patient's characteristics and of the accumulated experience. On the other hand, if a particular patient class is not sufficiently covered by cases, the use of rules may be exploited to try to learn suitable situations, in order to improve the competence of the case-based component. Such a 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.