Document Type
Journal Article
Publication Date
2016
Journal
Canadian Journal of Kidney Health and Disease
Volume
3
Inclusive Pages
11
DOI
10.1186/s40697-016-0099-4
Abstract
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.
APA Citation
Sutherland, S. M., Chawla, L. S., Kane-Gill, S. L., Hsu, R. K., Kramer, A. A., Goldstein, S. L., … on behalf of the 15 ADQI Consensus Group. (2016). Utilizing electronic health records to predict acute kidney injury risk and outcomes: workgroup statements from the 15th ADQI Consensus Conference. Canadian Journal of Kidney Health and Disease, 3, 11. http://doi.org/10.1186/s40697-016-0099-4
Peer Reviewed
1
Open Access
1
Comments
Reproduced with permission of BioMed Central Ltd. Canadian Journal of Kidney Health and Disease.