A Study of Complication Identification Based on Weighted Association Rule Mining

Abstract : With the fast development of big data technology, data mining algorithms are widely used to process the medical data and support clinical decision-making. In this paper, a new method is proposed to mine the disease association rule and predict the possible complications. The concept of disease concurrent weight is proposed and Back Propagation (BP) neural network model is applied to calculate the disease concurrent weight. Adopting the weighted association rule mining algorithm, diseases complication association rule are derived, which can help to remind doctors about patients’ potential complications. The empirical evaluation using hospital patients’ medical information shows that the proposed method is more effective than two baseline methods.
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Communication dans un congrès
Maria Cecilia Calani Baranauskas; Kecheng Liu; Lily Sun; Vânia Paula de Almeida Neris; Rodrigo Bonacin; Keiichi Nakata. 17th International Conference on Informatics and Semiotics in Organisations (ICISO), Aug 2016, Campinas, Brazil. Springer International Publishing, IFIP Advances in Information and Communication Technology, AICT-477, pp.149-158, 2016, Socially Aware Organisations and Technologies. Impact and Challenges. 〈10.1007/978-3-319-42102-5_17〉
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Zhijun Yan, Kai Liu, Meiming Xing, Tianmei Wang, Baowen Sun. A Study of Complication Identification Based on Weighted Association Rule Mining. Maria Cecilia Calani Baranauskas; Kecheng Liu; Lily Sun; Vânia Paula de Almeida Neris; Rodrigo Bonacin; Keiichi Nakata. 17th International Conference on Informatics and Semiotics in Organisations (ICISO), Aug 2016, Campinas, Brazil. Springer International Publishing, IFIP Advances in Information and Communication Technology, AICT-477, pp.149-158, 2016, Socially Aware Organisations and Technologies. Impact and Challenges. 〈10.1007/978-3-319-42102-5_17〉. 〈hal-01646565〉

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