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Erschienen in:
An X-ray of ICISO Portrayed Through the Lens of Actor-Network Theory Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

A Study of Complication Identification Based on Weighted Association Rule Mining

verfasst von : Zhijun Yan, Kai Liu, Meiming Xing, Tianmei Wang, Baowen Sun

Erschienen in: Socially Aware Organisations and Technologies. Impact and Challenges

Verlag: Springer International Publishing

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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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Vorheriges Kapitel A Pragmatic Approach to Disambiguation in Text Understanding
Nächstes Kapitel A Study on the Individualized Information Service Mode and Its Application in the Manufacturing and Supply Chain
Literatur
1.
Esfandiari, N., Babavalian, M.R., Moghadam, A.M.E., Tabar, V.K.: Knowledge discovery in medicine: current issue and future trend. Expert Syst. Appl. 41(9), 4434–4463 (2014)CrossRef
2.
Shea, S., Hripcsak, G.: Accelerating the use of electronic health records in physician practices. New Engl. J. Med. 362(3), 192–195 (2010)CrossRef
3.
Coloma, P.M., de Bie, S.: Data mining methods to detect sentinel associations and their application to drug safety surveillance. Curr. Epidemiol. Rep. 1(3), 110–119 (2014)CrossRef
4.
Murff, H.J., FitzHenry, F., Matheny, M.E., Gentry, N., Kotter, K.L., Crimin, K., Dittus, R.S., Rosen, A.K., Elkin, P.L., Brown, S.H.: Automated identification of postoperative complications within an electronic medical record using natural language processing. JAMA 306(8), 848–855 (2011)CrossRef
5.
Eshlaghy, A.T., Poorebrahimi, A., Ebrahimi, M., Razavi, A.R., Ahmad, L.G.: Using three machine learning techniques for predicting breast cancer recurrence. J. Health Med. Inf. 4(2), 124 (2013)
6.
Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nat. Rev. Genet. 13(6), 395–405 (2012)CrossRef
7.
Kwon, J.H., Lee, N., Park, J.Y., Yu, Y.S., Kim, J.P., Shin, J.H., Kim, D.S., Joh, J.W., Kim, D.S., Choi, K.Y.: Actionable gene expression-based patient stratification for molecular targeted therapy in hepatocellular carcinoma. PLoS ONE 8(6), e64260 (2013)CrossRef
8.
Roque, F.S., Jensen, P.B., Schmock, H., Dalgaard, M., Andreatta, M., Hansen, T., Søeby, K., Bredkjær, S., Juul, A., Werge, T.: Using electronic patient records to discover disease correlations and stratify patient cohorts. PLoS Comput. Biol. 7(8), e1002141 (2011)CrossRef
9.
Hanauer, D.A., Rhodes, D.R., Chinnaiyan, A.M.: Exploring clinical associations using ‘-omics’ based enrichment analyses. PLoS ONE 4(4), e5203 (2009)CrossRef
10.
Holmes, A.B., Hawson, A., Liu, F., Friedman, C., Khiabanian, H., Rabadan, R.: Discovering disease associations by integrating electronic clinical data and medical literature. PLoS ONE 6(6), e21132 (2011)CrossRef
11.
Tai, Y.M., Chiu, H.W.: Comorbidity study of ADHD: applying association rule mining (ARM) to National Health Insurance Database of Taiwan. Int. J. Med. Inform. 78(12), e75–e83 (2009)CrossRef
12.
Kim, H.S., Shin, A.M., Kim, M.K., Kim, Y.N.: Comorbidity study on type 2 diabetes mellitus using data mining. Korean J. Intern. Med. 27(2), 197–202 (2012)CrossRef
13.
Shin, A.M., Lee, I.H., Lee, G.H., Park, H.J., Park, H.S., Yoon, K.I., Lee, J.J., Kim, Y.N.: Diagnostic analysis of patients with essential hypertension using association rule mining. Healthc. Inform. Res. 16(2), 7–81 (2010)CrossRef
14.
Wright, A., Chen, E.S., Maloney, F.L.: An automated technique for identifying associations between medications, laboratory results and problems. J. Biomed. Inform. 43(6), 891–901 (2010)CrossRef
15.
Kuo, C.F.J., Hsu, C.T.M.Z., Liu, X., Wu, H.C.: Automatic inspection system of LED chip using two-stages back-propagation neural network. J. Intell. Manuf. 25(6), 1235–1243 (2014)CrossRef
16.
Wang, L., Zeng, Y., Chen, T.: Back propagation neural network with adaptive differential evolution algorithm for time series forecasting. Expert Syst. Appl. 42(2), 855–863 (2015)CrossRef
17.
Cai, C.H., Fu, A.W., Cheng, C., Kwong, W.: Mining association rules with weighted items. In: Proceedings of Database Engineering and Applications Symposium. IEEE (1998)
18.
Pears, R., Yun, S.K., Dobbie, G., Yeap, W.: Weighted association rule mining via a graph based connectivity model. Inf. Sci. 218(1), 61–84 (2013)MathSciNetCrossRefMATH
19.
Li, T., Li, X.: Novel alarm correlation analysis system based on association rules mining in telecommunication networks. Inf. Sci. 180(16), 2960–2978 (2010)CrossRef
20.
Hoque, N., Nath, B., Bhattacharyya, D.K.: A new approach on rare association rule mining. Int. J. Comput. Appl. 53(3), 1–6 (2012)CrossRef
Metadaten
Titel
A Study of Complication Identification Based on Weighted Association Rule Mining
verfasst von
Zhijun Yan
Kai Liu
Meiming Xing
Tianmei Wang
Baowen Sun
Copyright-Jahr
2016
Buch
Socially Aware Organisations and Technologies. Impact and Challenges

Print ISBN: 978-3-319-42101-8

Electronic ISBN: 978-3-319-42102-5

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-42102-5_17

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