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A Big Data Application of Machine Learning-Based Framework to Identify Type 2 Diabetes Through Electronic Health Records

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Knowledge Management in Organizations (KMO 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 731))

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Abstract

Studies of the genotype-phenotype associations for diseases such as type 2 diabetes mellitus (T2DM) become increasingly popular in recent years. Commonly used methods are genome-wide association study (GWAS) and phenome-wide association study (PheWAS). To perform the above analysis, it is necessary to identify T2DM subjects’ cases and controls based on electronic health records (EHR). However, the existing expert-based identification algorithms often have a low recall and miss a large number of the valuable samples under conservative filtering standards. As a pilot study, this paper proposed a semi-automated framework based on machine learning. We target to optimize the filtering criteria to improve recall at the same time keeping low false-positive rate. We validate the proposed framework using a EHR database with ten years of records and show the effectiveness of the proposed framework.

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References

  1. Centers for Disease Control and Prevention, National Diabetes Statistics Report: Estimates of Diabetes and Its Burden in the United States, US Department of Health and Human Services, Atlanta, GA(2014)

    Google Scholar 

  2. Xu, Y., Wang, L., He, J.: Prevalence and control of diabetes in Chinese adults. JAMA 310(9), 948–959 (2013)

    Article  Google Scholar 

  3. Zheng, T., Chen, Y., et al.: A machine learning-based framework to identify type 2 diabetes through electronic health records. Int. J. Med. Inform. 97, 120–127 (2017)

    Article  Google Scholar 

  4. Kho, A.N., et al.: Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study. J. Am. Med. Inform. Assoc. 19(2), 212–218 (2012)

    Article  Google Scholar 

  5. Grant, R.W., et al.: Practice-linked online personal health records for type 2 diabetes mellitus. JAMA Intern. Med. 168(16), 1776–1782 (2008)

    Article  Google Scholar 

  6. Wei, W.Q., et al.: Impact of data fragmentation across healthcare centers on the accuracy of a high-throughput clinical phenotyping algorithm for specifying subjects with type 2 diabetes mellitus. J. Am. Med. Inform. Assoc. 19(2), 219–224 (2012)

    Article  Google Scholar 

  7. Marinov, M., Mosa, A., Yoo, I., Boren, S.A.: Data-mining technologies for diabetes: a systematic review. J. Diabetes Sci. Technol. 5(6), 1549–1556 (2011)

    Article  Google Scholar 

  8. Mani, S., Chen, Y., Elasy, T., Clayton, W., Denny, J.: Type 2 diabetes risk forecasting from EMR data using machine learning. AMIA Ann. Symp. Proc. 2012, 606–615 (2012)

    Google Scholar 

  9. Huang, Y., McCullagh, P., Black, N., Harper, R.: Feature selection and classification model construction on type 2 diabetic patients’ data. Artif. Intell. Med. 41(3), 251–262 (2007)

    Article  Google Scholar 

  10. Wang, Y., Sung, P., Lin, P., Yu, Y., Chung, R.: A multi-SNP association test for complex diseases incorporating an optimal P-value threshold algorithm in nuclear families. BMC Genom. 16, 381 (2015)

    Article  Google Scholar 

  11. Mitteroecker, P., Cheverud, J.M., Pavlicev, M.: Multivariate analysis of genotype-phenotype association. Genetics 203, 3 (2016)

    Google Scholar 

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Acknowledgments

We thank members of the Changning regional distributed EHR network, Shanghai, China, for sharing of the de-identified EHR data evaluated in this paper. We would like to thank the members of the Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA and Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA.

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Authors and Affiliations

  1. Institute of Image Communication and Networking, Shanghai Jiao Tong University, Shanghai, China

    Tao Zheng & Ya Zhang

  2. Tongren Hospital, Shanghai Jiao Tong University, Shanghai, China

    Tao Zheng

Authors
  1. Tao Zheng
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  2. Ya Zhang
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Corresponding author

Correspondence to Tao Zheng .

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Editors and Affiliations

  1. University of Staffordshire, Stoke-on-Trent, Staffordshire, United Kingdom

    Lorna Uden

  2. Beijing Jiaotong University, Beijing, China

    Wei Lu

  3. Department of Information Management, University of Kaohsiung, Kaohsiung, Taiwan

    I-Hsien Ting

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Zheng, T., Zhang, Y. (2017). A Big Data Application of Machine Learning-Based Framework to Identify Type 2 Diabetes Through Electronic Health Records. In: Uden, L., Lu, W., Ting, IH. (eds) Knowledge Management in Organizations. KMO 2017. Communications in Computer and Information Science, vol 731. Springer, Cham. https://doi.org/10.1007/978-3-319-62698-7_37

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  • DOI: https://doi.org/10.1007/978-3-319-62698-7_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62697-0

  • Online ISBN: 978-3-319-62698-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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