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Utilizing electronic health records to predict acute kidney injury risk and outcomes: Workgroup statements from the 15 <sup>th</sup> ADQI Consensus Conference

Sutherland, SM and Chawla, LS and Kane-Gill, SL and Hsu, RK and Kramer, AA and Goldstein, SL and Kellum, JA and Ronco, C and Bagshaw, SM (2016) Utilizing electronic health records to predict acute kidney injury risk and outcomes: Workgroup statements from the 15 <sup>th</sup> ADQI Consensus Conference. Canadian Journal of Kidney Health and Disease, 3 (1).

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Abstract

© 2016 Sutherland et al. The data contained within the electronic health record (EHR) is "big" from the standpoint of volume, velocity, and variety. These circumstances and the pervasive trend towards EHR adoption have sparked interest in applying big data predictive analytic techniques to EHR data. Acute kidney injury (AKI) is a condition well suited to prediction and risk forecasting; not only does the consensus definition for AKI allow temporal anchoring of events, but no treatments exist once AKI develops, underscoring the importance of early identification and prevention. The Acute Dialysis Quality Initiative (ADQI) convened a group of key opinion leaders and stakeholders to consider how best to approach AKI research and care in the "Big Data" era. This manuscript addresses the core elements of AKI risk prediction and outlines potential pathways and processes. We describe AKI prediction targets, feature selection, model development, and data display.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Sutherland, SM
Chawla, LS
Kane-Gill, SL
Hsu, RK
Kramer, AA
Goldstein, SL
Kellum, JAkellum@pitt.eduKELLUM0000-0003-1995-2653
Ronco, C
Bagshaw, SM
Date: 26 February 2016
Date Type: Publication
Journal or Publication Title: Canadian Journal of Kidney Health and Disease
Volume: 3
Number: 1
DOI or Unique Handle: 10.1186/s40697-016-0099-4
Schools and Programs: School of Medicine > Clinical and Translational Science
School of Medicine > Critical Care Medicine
School of Pharmacy > Pharmaceutical Sciences
Refereed: Yes
Article Type: Review
Date Deposited: 22 Aug 2016 18:49
Last Modified: 13 Apr 2019 13:55
URI: http://d-scholarship.pitt.edu/id/eprint/28727

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