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Machine Learning: Multi-site Evidence-Based Best Practice Discovery

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Book cover Machine Learning, Optimization, and Big Data (MOD 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10122))

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Abstract

This study establishes interoperability among electronic medical records from 737 healthcare sites and performs machine learning for best practice discovery. A mapping algorithm is designed to disambiguate free text entries and to provide a unique and unified way to link content to structured medical concepts despite the extreme variations that can occur during clinical diagnosis documentation. Redundancy is reduced through concept mapping. A SNOMED-CT graph database is created to allow for rapid data access and queries. These integrated data can be accessed through a secured web-based portal. A classification model (DAMIP) is then designed to uncover discriminatory characteristics that can predict the quality of treatment outcome. We demonstrate system usability by analyzing Type II diabetic patients. DAMIP establishes a classification rule on a training set which results in greater than 80% blind predictive accuracy on an independent set of patients. By including features obtained from structured concept mapping, the predictive accuracy is improved to over 88%. The results facilitate evidence-based treatment and optimization of site performance through best practice dissemination and knowledge transfer.

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Acknowledgment

This paper receives the 2016 NSF Health Organization Transformation award (second place). The work is partially supported by a grant from the National Science Foundation IIP-1361532. Findings and conclusions in this paper are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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Authors and Affiliations

  1. Center for Operations Research in Medicine and HealthCare, Atlanta, Georgia, USA

    Eva K. Lee, Yuanbo Wang, Matthew S. Hagen & Xin Wei

  2. NSF I/UCRC Center for Health Organization Transformation, Atlanta, Georgia, USA

    Eva K. Lee, Yuanbo Wang, Matthew S. Hagen & Xin Wei

  3. Georgia Institute of Technology, Atlanta, Georgia, USA

    Eva K. Lee, Yuanbo Wang, Matthew S. Hagen & Xin Wei

  4. University of South Carolina School of Medicine, Greenville, South Carolina, USA

    Robert A. Davis & Brent M. Egan

  5. Care Coordination Institute, Greenville, South Carolina, USA

    Robert A. Davis & Brent M. Egan

Authors
  1. Eva K. Lee
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  2. Yuanbo Wang
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  3. Matthew S. Hagen
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  4. Xin Wei
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  5. Robert A. Davis
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  6. Brent M. Egan
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Corresponding author

Correspondence to Eva K. Lee .

Editor information

Editors and Affiliations

  1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  2. Semantic Technology Laboratory, National Research Council (CNR), Catania, Italy

    Piero Conca

  3. Dipartimento di Sociologia e Metodi della Ricerca Sociale, Università di Catania, Catania, Italy

    Giovanni Giuffrida

  4. Department of Mathematics and Computer Science, University of Catania, Catania, Italy

    Giuseppe Nicosia

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© 2016 Springer International Publishing AG

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Lee, E.K., Wang, Y., Hagen, M.S., Wei, X., Davis, R.A., Egan, B.M. (2016). Machine Learning: Multi-site Evidence-Based Best Practice Discovery. In: Pardalos, P., Conca, P., Giuffrida, G., Nicosia, G. (eds) Machine Learning, Optimization, and Big Data. MOD 2016. Lecture Notes in Computer Science(), vol 10122. Springer, Cham. https://doi.org/10.1007/978-3-319-51469-7_1

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  • DOI: https://doi.org/10.1007/978-3-319-51469-7_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-51468-0

  • Online ISBN: 978-3-319-51469-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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