Case-based reasoning
A framework for knowledge-based temporal abstraction
Artificial Intelligence
Are Case-Based Reasoning and Dissimilarity-Based Classification Two Sides of the Same Coin?
MLDM '01 Proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition
Using Configuration Techniques for Adaptation
Case-Based Reasoning Technology, From Foundations to Applications
Temporal Abstractions for Diabetic Patients Management
AIME '97 Proceedings of the 6th Conference on Artificial Intelligence in Medicine in Europe
Therapy Planning Using Qualtitative Trend Descriptions
AIME '95 Proceedings of the 5th Conference on Artificial Intelligence in Medicine in Europe: Artificial Intelligence Medicine
MacRad: Radiology Image Resource with a Case-Based Retrieval System
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Temporal Abstractions and Case-Based Reasoning for Medical Course Data: Two Prognostic Applications
MLDM '01 Proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition
International Journal of Knowledge-based and Intelligent Engineering Systems
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The goal of the TeCoMed project is to compute early warnings against forthcoming waves or even epidemics of infectious diseases, especially of influenza, and to send them to interested practitioners, pharmacists etc. in the German federal state of Mecklenburg-Western Pomerania. Usually, each winter one influenza wave can be observed in Germany. In some years they are nearly unnoticeable, while in other years doctors and pharmacists even run out of vaccine. Because of the irregular cyclic behaviour it is insufficient to determine average values based on former years and to give warnings as soon as such values are noticeably overstepped. So, we have developed a method that combines Temporal Abstraction with Case-based Reasoning. The idea is to search for former, similar cases and to make use of them for the decision whether early warning is appropriate or not.