Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records

Author:

Tomašev NenadORCID,Harris Natalie,Baur Sebastien,Mottram Anne,Glorot XavierORCID,Rae Jack W.,Zielinski Michal,Askham Harry,Saraiva Andre,Magliulo Valerio,Meyer Clemens,Ravuri Suman,Protsyuk Ivan,Connell AlistairORCID,Hughes Cían O.ORCID,Karthikesalingam Alan,Cornebise JulienORCID,Montgomery Hugh,Rees GeraintORCID,Laing Chris,Baker Clifton R.,Osborne Thomas F.ORCID,Reeves Ruth,Hassabis Demis,King Dominic,Suleyman Mustafa,Back Trevor,Nielson Christopher,Seneviratne Martin G.ORCID,Ledsam Joseph R.ORCID,Mohamed ShakirORCID

Publisher

Springer Science and Business Media LLC

Subject

General Biochemistry, Genetics and Molecular Biology

Reference97 articles.

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3. Sutton, R. T. et al. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit. Med. 3, 17 (2020).

4. Johnson, A. E. W. & Mark, R. G. Real-time mortality prediction in the Intensive Care Unit. AMIA Annu. Symp. Proc. 2017, 994–1003 (2017).

5. Barnes, S., Hamrock, E., Toerper, M., Siddiqui, S. & Levin, S. Real-time prediction of inpatient length of stay for discharge prioritization. J. Am. Med. Inform. Assoc. 23, e2–e10 (2016).

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