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

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

Computational Statistics & Data Analysis, 2017, vol. 112, issue C, 63-79

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.

Keywords: Time series; Missing value; Tolerance rough set; Particle swarm optimization; Inverse distance weight (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:112:y:2017:i:c:p:63-79

DOI: 10.1016/j.csda.2017.02.012

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