C4.5: programs for machine learning
C4.5: programs for machine learning
Decision trees based on automatic learning and their use in cardiology
Journal of Medical Systems
Estimating Hospital Inefficiency: Does Case Mix Matter?
Journal of Medical Systems
Trends in Hospital Efficiency Among Metropolitan Markets
Journal of Medical Systems
Towards More Optimal Medical Diagnosing with Evolutionary Algorithms
Journal of Medical Systems
Explorer's Guide to the Semantic Web
Explorer's Guide to the Semantic Web
Knowledge discovery with classification rules in a cardiovascular dataset
Computer Methods and Programs in Biomedicine
Direct manipulation of free form deformation in evolutionary design optimisation
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Adoption of Semantic Web Technologies for Developing Medical Software Systems and Services
Proceedings of the 2010 conference on Information Modelling and Knowledge Bases XXI
Automating ontology based information integration using service orientation
WSEAS Transactions on Computers
Expert Systems with Applications: An International Journal
ADNTIIC'11 Proceedings of the Second international conference on Advances in New Technologies, Interactive Interfaces and Communicability
Automated Diagnosis Through Ontologies and Logical Descriptions: The ADONIS Approach
International Journal of Decision Support System Technology
Load-sensitive dynamic workflow re-orchestration and optimisation for faster patient healthcare
Computer Methods and Programs in Biomedicine
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In this paper we study the optimization of medical diagnostic process from the data access point of view. According to many studies which showed that optimized diagnostic process can considerably improve efficiency in health care industry, we present a new approach to data integration within a diagnostic process. It is our belief that a unified access to data resources throughout the whole diagnostic process considerably improves the efficiency of the process itself. When combining the optimized data access with an existing algorithmic optimization method an optimized process can be achieved that takes into account the quality of a diagnosis, the individual needs of each patient, the associated costs, and the utilization of personnel/equipment. To enable an efficient management of data, we developed a semantic web based system for the integration of data resources within a medical diagnostic process. Then we combined the unified data access with our existing diagnostic process optimization framework that uses machine learning techniques and evolutionary algorithms. The new defined diagnostic process framework is finally used in a case-study for optimizing the diagnosing of the mitral valve prolapse syndrome in a regional hospital department.