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Using Artificial Intelligence to Predict Future Acute Kidney Injury

Thursday, August 1, 2019

Submitted by

Mark Ferguson

Source

Source Name: Nature

Source URL: https://www.nature.com/articles/s41586-019-1390-1

Author(s)

Nenad Tomašev, Xavier Glorot, Jack W. Rae, Michal Zielinski, Harry Askham, Andre Saraiva, Anne Mottram, Clemens Meyer, Suman Ravuri, Ivan Protsyuk, Alistair Connell, Cían O. Hughes, Alan Karthikesalingam, Julien Cornebise, Hugh Montgomery, Geraint Rees, Chris Laing, Clifton R. Baker, Kelly Peterson, Ruth Reeves, Demis Hassabis, Dominic King, Mustafa Suleyman, Trevor Back, Christopher Nielson, Joseph R. Ledsam, Shakir Mohamed

Using deep learning employing data from over 700,000 patients (6 billion data points), an algorithm for continuous prediction of the risk of acute kidney injury (AKI) was developed. The model correctly predicted over 90% of AKI requiring dialysis with a lead time of up to 48 hours, with 2 false alerts for every true alert.