Abstract
To boost the accuracy of clinical decision support systems and degrade their misdiagnosis rates, a hybrid model was proposed with Bayesian networks (BN) and case-based reasoning (CBR). BN were constructed with the feature attributes and their casual relationships were learned. The similarities of feature attributes were measured with the case matching method, as well as the knowledge of their dependent relationships. Therefore, the accuracy of the diagnosis system was enriched through the dynamic retrieval method.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (71171143), by Tianjin Research Program of Application Foundation and Advanced Technology (10JCYBJC07300), and Science and Technology Program of FOXCONN Group (120024001156).
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Xu, M., Shen, J. (2013). Clinical Decision Support Model of Heart Disease Diagnosis Based on Bayesian Networks and Case-Based Reasoning. In: Qi, E., Shen, J., Dou, R. (eds) The 19th International Conference on Industrial Engineering and Engineering Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38391-5_23
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DOI: https://doi.org/10.1007/978-3-642-38391-5_23
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