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International Conference on Data Integration in the Life Sciences

DILS 2015: Data Integration in the Life Sciences pp 208–222Cite as

Reverse Engineering Measures of Clinical Care Quality: Sequential Pattern Mining

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9162))

Abstract

Pattern mining has been applied to classification problems. However, detection and analysis of frequently occurring patterns in clinical data is less studied. Instead, data-driven measures of the quality of clinical care are based on abstractions from clinical guidelines, and often are not validated on the basis of outcomes. We hypothesize that by using outcomes as a training signal, we can discover patterns of treatment that lead to better or worse than expected outcomes. Because clinical data is often censored, traditional classification algorithms are inappropriate. In addition, it is difficult to infer the latent meanings of patterns in clinical data if frequency is the only explanation. In this paper, we present a framework for discovering critical patterns in censored data. We evaluate this framework by comparing the patterns we detect with guidelines. Our framework can improve the accuracy in survival analysis and facilitate discovery of patterns of care that improve outcomes.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of California, Los Angeles, USA

    Hsuan Chiu

  2. Department of Preventive Medicine, University of Southern California, Los Angeles, USA

    Daniella Meeker

Authors
  1. Hsuan Chiu
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  2. Daniella Meeker
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Corresponding author

Correspondence to Hsuan Chiu .

Editor information

Editors and Affiliations

  1. USC Stevens Neuroimaging and Informatics Institute, Los Angeles, California, USA

    Naveen Ashish

  2. USC Information Sciences Institute, Marina del Rey, California, USA

    Jose-Luis Ambite

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© 2015 Springer International Publishing Switzerland

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Chiu, H., Meeker, D. (2015). Reverse Engineering Measures of Clinical Care Quality: Sequential Pattern Mining. In: Ashish, N., Ambite, JL. (eds) Data Integration in the Life Sciences. DILS 2015. Lecture Notes in Computer Science(), vol 9162. Springer, Cham. https://doi.org/10.1007/978-3-319-21843-4_17

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  • DOI: https://doi.org/10.1007/978-3-319-21843-4_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21842-7

  • Online ISBN: 978-3-319-21843-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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