A bio-statistical mining approach for classifying multivariate clinical time series data observed at irregular intervals

作者：

Highlights：

• Temporal mining framework for classifying unevenly spaced clinical time series data.

• Framework provides: temporal pre-processing, attribute selection and classification.

• Fuzzy Inference Double Exponential Smoothing method is proposed for pre-processing.

• Temporal pattern based tolerance rough set algorithm is presented for attribute selection.

• Decision tree classifier with temporal pattern induced gain ratio is used for classification.

摘要

•Temporal mining framework for classifying unevenly spaced clinical time series data.•Framework provides: temporal pre-processing, attribute selection and classification.•Fuzzy Inference Double Exponential Smoothing method is proposed for pre-processing.•Temporal pattern based tolerance rough set algorithm is presented for attribute selection.•Decision tree classifier with temporal pattern induced gain ratio is used for classification.

论文关键词：Clinical time series,Fuzzy,Double exponential smoothing,Tolerance rough set,Data mining,Decision tree

论文评审过程：Received 26 September 2016, Revised 27 January 2017, Accepted 28 January 2017, Available online 6 February 2017, Version of Record 21 February 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.01.056