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Automatically Detecting AKI Events from Primary Care Records

Tirunagari, Santosh, Bull, S, Christopher, F, de Lusignan, Simon and Poh, Norman (2017) Automatically Detecting AKI Events from Primary Care Records In: BRS 2017 Conference, 2017-04-26 - 2017-04-28, Nottingham, UK.

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Abstract

Introduction: 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). Due to the potentially irreversible nature of the damage AKI episodes cause to renal function, their detection can play a significant role in predicting a kidney’s effectiveness. Although algorithms for the detection of AKI are available for patients under constant monitoring, e.g. inpatients, their applicability to primary care settings is less clear as patients’ eGFR often contains large lapses in time between measurements. We therefore present two alternative automated approaches for detecting AKI: using the novel Surrey AKI detection algorithm (SAKIDA) (Figure a) and as the outlier points when using Gaussian process regression (GPR) (Figure b).

Item Type: Conference or Workshop Item (Conference Poster)
Subjects : Computer Science
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
NameEmailORCID
Tirunagari, Santoshsantosh.tirunagari@surrey.ac.ukUNSPECIFIED
Bull, SUNSPECIFIEDUNSPECIFIED
Christopher, FUNSPECIFIEDUNSPECIFIED
de Lusignan, SimonS.Lusignan@surrey.ac.ukUNSPECIFIED
Poh, NormanN.Poh@surrey.ac.ukUNSPECIFIED
Date : 2017
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 10 Mar 2017 15:42
Last Modified : 31 Oct 2017 19:11
URI: http://epubs.surrey.ac.uk/id/eprint/813747

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