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International Conference on Artificial Intelligence in Medicine

AIME 2020: Artificial Intelligence in Medicine pp 414–424Cite as

Transitive Sequential Pattern Mining for Discrete Clinical Data

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12299))

Abstract

Electronic health records (EHRs) contain important temporal information about disease progression and patients. However, mining temporal representations from discrete EHR data (e.g., diagnosis, medication, or procedure codes) for use in standard Machine Learning is challenging. We propose a transitive Sequential Pattern Mining approach (tSPM) to address the temporal irregularities involved in recording discrete records in EHRs. We perform experiments to compare the classification performance metrics for predicting “true” diagnosis between traditional sequential pattern mining (SPM) and the proposed tSPM algorithms across multiple diseases. We demonstrate that transitive approach is superior to the traditional SPM in mining temporal representations for diagnosis prediction.

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Acknowledgements

The work in this paper was supported by NIH grant R01-HG009174. The content of the paper is solely the responsibility of the authors and does not necessarily represent the official views of NIH.

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Authors and Affiliations

  1. Massachusetts General Hospital, Boston, MA, 02144, USA

    Hossein Estiri & Shawn N. Murphy

  2. Harvard Medical School, Boston, MA, 02155, USA

    Hossein Estiri & Shawn N. Murphy

  3. Department of Mathematics, Harvard University, Cambridge, MA, 02138, USA

    Sebastien Vasey

Authors
  1. Hossein Estiri
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  2. Sebastien Vasey
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  3. Shawn N. Murphy
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Corresponding author

Correspondence to Hossein Estiri .

Editor information

Editors and Affiliations

  1. School of Nursing, University of Minnesota, Minneapolis, MN, USA

    Martin Michalowski

  2. Ben-Gurion University of the Negev, Tonawanda, NY, USA

    Robert Moskovitch

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Estiri, H., Vasey, S., Murphy, S.N. (2020). Transitive Sequential Pattern Mining for Discrete Clinical Data. In: Michalowski, M., Moskovitch, R. (eds) Artificial Intelligence in Medicine. AIME 2020. Lecture Notes in Computer Science(), vol 12299. Springer, Cham. https://doi.org/10.1007/978-3-030-59137-3_37

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  • DOI: https://doi.org/10.1007/978-3-030-59137-3_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59136-6

  • Online ISBN: 978-3-030-59137-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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