Automatic detection of acute kidney injury episodes from primary care data

Santosh Tirunagari, Simon C. Bull, Aki Vehtari, Christopher Farmer, Simon De Lusignan, Norman Poh

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)

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 the detection of AKI 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 before AKI 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 from direct observation of eGFR signals. 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 we entitle Surrey AKI detection algorithm (SAKIDA). Using SAKIDA, we can identify the number of AKI episodes a patient experiences with an accuracy of 70%, when evaluated against the performance of human experts.

Original languageEnglish
Title of host publication2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
PublisherIEEE
Number of pages6
ISBN (Electronic)9781509042401
DOIs
Publication statusPublished - 9 Feb 2017
MoE publication typeA4 Article in a conference publication
EventIEEE Symposium Series on Computational Intelligence - Athens, Greece
Duration: 6 Dec 20169 Dec 2016

Conference

ConferenceIEEE Symposium Series on Computational Intelligence
Abbreviated titleSSCI
CountryGreece
CityAthens
Period06/12/201609/12/2016

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  • Cite this

    Tirunagari, S., Bull, S. C., Vehtari, A., Farmer, C., De Lusignan, S., & Poh, N. (2017). Automatic detection of acute kidney injury episodes from primary care data. In 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 [7849885] IEEE. https://doi.org/10.1109/SSCI.2016.7849885