Planning and control
Derivational Analogy in PRODIGY: Automating Case Acquisition, Storage, and Utilization
Machine Learning - Special issue on case-based reasoning
Intelligent planning: a decomposition and abstraction based approach
Intelligent planning: a decomposition and abstraction based approach
Guest Editors‘ Introduction: On Applied Research in MachineLearning
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Handbook on Ontologies (International Handbooks on Information Systems)
Handbook on Ontologies (International Handbooks on Information Systems)
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Practical Statistics for Medical Research
Practical Statistics for Medical Research
Temporal abstraction in intelligent clinical data analysis: A survey
Artificial Intelligence in Medicine
Special issue on case-based reasoning in the health sciences
Applied Intelligence
Hierarchical Classifiers for Complex Spatio-temporal Concepts
Transactions on Rough Sets IX
Case-based reasoning in the health sciences: What's next?
Artificial Intelligence in Medicine
Automatic planning of treatment of infants with respiratory failure through rough set modeling
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
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We discuss medical treatment planning in the context of case-based planning, where plans (of treatment) are treated as complex decisions. A plan for a particular case is constructed from known plans for similar training examples. In order to evaluate and improve the prediction quality of complex decisions, we use a method for approximation of similarity measure between plans. The method makes it possible to transform the acquired domain knowledge about similarities of plans, expressed by medical experts in natural language, to a low level language understandable by the system. To accomplish this task, we developed a method for approximation of the ontology of concepts expressed by medical experts. We present two applications of the ontology approximation, namely, for approximation of similarity between patient histories and for approximation of compatibility of patient histories with planned therapies. Next, we use these concept approximations to define two measures on which are based two methods for (plan) therapy prediction. The article includes results of experiments with these methods performed on medical data obtained from Neonatal Intensive Care Unit, First Department of Pediatrics, Polish-American Institute of Pediatrics, Collegium Medicum, Jagiellonian University, Krak?ow, Poland. The experiments are pertained to the identification of infants' death risk caused by respiratory failure.