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Automatic Detection of Acute Kidney Injury Episodes from Primary Care Data

Tirunagari, Santosh, Bull, SC and Poh, Norman (2016) Automatic Detection of Acute Kidney Injury Episodes from Primary Care Data In: IEEE SSCI CICARE 2016, 2016-12-06 - 2016-12-09, Athens, Greece.

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Abstract

—Acute kidney injury (AKI) is characterised by a rapid deterioration in kidney function, and can be identified by examining the rate of change in a patient’s estimated glomerular filtration rate (eGFR) signal. Due to the potentially irreversible nature of the damage AKI episodes cause to renal function, their detection plays a significant role in predicting a kidney’s effectiveness. Although algorithms for this are available for patients under constant monitoring, e.g. inpatients, their applicability to primary care settings is less clear as the eGFR signal often contains large lapses in time between measurements. However, waiting for hospital admittance is undesirable, as detecting AKI early can help to mitigate the degradation of kidney function and the associated increase in morbidity and mortality. Traditionally, a clinician in a primary care setting would manually identify AKI episodes within an eGFR signal. While this approach may work for individual patients, the time consuming nature of it precludes quick large-scale monitoring. We therefore present two alternative automated approaches for detecting AKI: as the outlier points when using Gaussian process regression and using a novel technique called Surrey AKI detection algorithm (SAKIDA). Using SAKIDA, we can identify the exact number of AKI episodes a patient experiences with an accuracy of 70%, when evaluated against the performance of human experts.

Item Type: Conference or Workshop Item (Conference Paper)
Subjects : Computer Science
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
NameEmailORCID
Tirunagari, Santoshsantosh.tirunagari@surrey.ac.ukUNSPECIFIED
Bull, SCUNSPECIFIEDUNSPECIFIED
Poh, NormanN.Poh@surrey.ac.ukUNSPECIFIED
Date : 2016
Identification Number : 10.1109/SSCI.2016.7849885
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 14:51
Last Modified : 07 Jul 2017 14:35
URI: http://epubs.surrey.ac.uk/id/eprint/812516

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