Principles of data mining
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
Health Decision Support Systems
Health Decision Support Systems
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Decision Support Systems and Intelligent Systems (7th Edition)
Decision Support Systems and Intelligent Systems (7th Edition)
An intelligent system for customer targeting: a data mining approach
Decision Support Systems
Decision Support Systems - Special issue: Data mining for financial decision making
Decision station: situating decision support systems
Decision Support Systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Clinical Decision Support Systems: Theory and Practice (Health Informatics)
Clinical Decision Support Systems: Theory and Practice (Health Informatics)
Adaptive decision support for intensive care
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
Data mining techniques for cancer detection using serum proteomic profiling
Artificial Intelligence in Medicine
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Clinical Decision Support Systems (CDSS) are becoming commonplace. They are used to alert doctors about drug interactions, to suggest possible diagnostics and in several other clinical situations. One of the approaches to building CDSS is by using techniques from the Knowledge Discovery from Databases (KDD) area. However using KDD for the construction of the knowledge base used in such systems, while reducing the maintenance work still demands repeated human intervention. In this work we present a KDD based architecture for CDSS for intensive care medicine. By resorting to automated data acquisition our architecture allows for the evaluation of the predictions made and subsequent action aiming at improving the predictive performance thus closing the KDD loop.