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Imputing missing values in unevenly spaced clinical time series data to build an effective temporal classification framework

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  • Nancy, Jane Y.
  • Khanna, Nehemiah H.
  • Arputharaj, Kannan

Abstract

BACKGROUND: In healthcare domain, clinical trials generate time-stamped data that record set of observations on patient health status. These data are liable to missing values since there are situations, where the patient observations are neither done regularly nor updated correctly.

Suggested Citation

  • Nancy, Jane Y. & Khanna, Nehemiah H. & Arputharaj, Kannan, 2017. "Imputing missing values in unevenly spaced clinical time series data to build an effective temporal classification framework," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 63-79.
    • Handle: RePEc:eee:csdana:v:112:y:2017:i:c:p:63-79
    DOI: 10.1016/j.csda.2017.02.012
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    References listed on IDEAS

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