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Automatic Classification of Irregularly Sampled Time Series with Unequal Lengths: A Case Study on Estimated Glomerular Filtration Rate

Tirunagari, S, Bull, S and Poh, Norman (2016) Automatic Classification of Irregularly Sampled Time Series with Unequal Lengths: A Case Study on Estimated Glomerular Filtration Rate In: 2016 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2016), 2016-09-13 - 2016-09-16, Salerno, Italy.

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Abstract

A patient’s estimated glomerular filtration rate (eGFR) can provide important information about disease progression and kidney function. Traditionally, an eGFR time series is interpreted by a human expert labelling it as stable or unstable. While this approach works for individual patients, the time consuming nature of it precludes the quick evaluation of risk in large numbers of patients. However, automating this process poses significant challenges as eGFR measurements are usually recorded at irregular intervals and the series of measurements differs in length between patients. Here we present a two-tier system to automatically classify an eGFR trend. First, we model the time series using Gaussian process regression (GPR) to fill in ‘gaps’ by resampling a fixed size vector of fifty time-dependent observations. Second, we classify the resampled eGFR time series using a K-NN/SVM classifier, and evaluate its performance via 5-fold cross validation. Using this approach we achieved an F-score of 0.90, compared to 0.96 for 5 human experts when scored amongst themselves.

Item Type: Conference or Workshop Item (Conference Poster)
Subjects : Computer Science
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
NameEmailORCID
Tirunagari, SUNSPECIFIEDUNSPECIFIED
Bull, SUNSPECIFIEDUNSPECIFIED
Poh, NormanN.Poh@surrey.ac.ukUNSPECIFIED
Date : 10 November 2016
Identification Number : 10.1109/MLSP.2016.7738901
Copyright Disclaimer : © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Contributors :
ContributionNameEmailORCID
UNSPECIFIEDIEEE, UNSPECIFIEDUNSPECIFIED
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 19 Oct 2016 15:29
Last Modified : 31 Oct 2017 18:48
URI: http://epubs.surrey.ac.uk/id/eprint/812518

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