Title |
Utilizing electronic health records to predict acute kidney injury risk and outcomes: workgroup statements from the 15th ADQI Consensus Conference
|
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Published in |
Canadian Journal of Kidney Health and Disease, February 2016
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DOI | 10.1186/s40697-016-0099-4 |
Pubmed ID | |
Authors |
Scott M. Sutherland, Lakhmir S. Chawla, Sandra L. Kane-Gill, Raymond K. Hsu, Andrew A. Kramer, Stuart L. Goldstein, John A. Kellum, Claudio Ronco, Sean M. Bagshaw, on behalf of the 15 ADQI Consensus Group |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 2 | 33% |
United Kingdom | 1 | 17% |
Unknown | 3 | 50% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 3 | 50% |
Scientists | 2 | 33% |
Practitioners (doctors, other healthcare professionals) | 1 | 17% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 2 | 2% |
Unknown | 125 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Master | 25 | 20% |
Researcher | 19 | 15% |
Student > Ph. D. Student | 18 | 14% |
Student > Bachelor | 10 | 8% |
Student > Doctoral Student | 7 | 6% |
Other | 26 | 20% |
Unknown | 22 | 17% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 40 | 31% |
Computer Science | 17 | 13% |
Nursing and Health Professions | 14 | 11% |
Engineering | 9 | 7% |
Business, Management and Accounting | 6 | 5% |
Other | 16 | 13% |
Unknown | 25 | 20% |