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Association Rule Learning and Frequent Sequence Mining of Cancer Diagnoses in New York State

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Data Management and Analytics for Medicine and Healthcare (DMAH 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10494))

Abstract

Analyzing large scale diagnosis histories of patients could help to discover comorbidity or disease progression patterns. Recently, open data initiatives make it possible to access statewide patient data at individual level, such as New York State SPARCS data. The goal of this study is to explore frequent disease co-occurrence and sequence patterns of cancer patients in New York State using SPARCS data. Our collection includes 18,208,830 discharge records from 1,565,237 patients with cancer-related diagnoses during 2011–2015. We use Apriori algorithm to discover top disease co-occurrences for common cancer categories based on support. We generate top frequent sequences of diagnoses with at least one cancer related diagnosis from patients’ diagnosis histories using the cSPADE algorithm. Our data driven approach provides essential knowledge to support the investigation of disease co-occurrence and progression patterns for improving the management of multiple diseases.

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Acknowledgments

This work is supported in part by NSF ACI 1443054, by NSF IIS 1350885 and by NSF IIP1069147.

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Authors and Affiliations

  1. Department of Computer Science, Stony Brook University, Stony Brook, NY, 11794, USA

    Yu Wang & Fusheng Wang

  2. Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, 11794, USA

    Fusheng Wang

Authors
  1. Yu Wang
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  2. Fusheng Wang
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Correspondence to Fusheng Wang .

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Editors and Affiliations

  1. Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA

    Edmon Begoli

  2. Stony Brook University, Stony Brook, New York, USA

    Fusheng Wang

  3. University of Washington, Seattle, Washington, USA

    Gang Luo

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Wang, Y., Wang, F. (2017). Association Rule Learning and Frequent Sequence Mining of Cancer Diagnoses in New York State. In: Begoli, E., Wang, F., Luo, G. (eds) Data Management and Analytics for Medicine and Healthcare. DMAH 2017. Lecture Notes in Computer Science(), vol 10494. Springer, Cham. https://doi.org/10.1007/978-3-319-67186-4_10

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  • DOI: https://doi.org/10.1007/978-3-319-67186-4_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67185-7

  • Online ISBN: 978-3-319-67186-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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