Cited by 1,086Machine Learning in HealthcareAcute Kidney Injury ResearchArtificial Intelligence in Healthcare

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A clinically applicable approach to continuous prediction of future acute kidney injury

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Tomašev, Nenad, Glorot, Xavier, Rae, Jack W., Zielinski, Michal, Askham, Harry, Saraiva, Andre, Mottram, Anne, Meyer, Clemens, Ravuri, Suman, Protsyuk, Ivan, Connell, Alistair, Hughes, Cían O., Karthikesalingam, Alan, Cornebise, Julien, Montgomery, Hugh, Rees, Geraint, Laing, Chris, Baker, Clifton R., Peterson, Kelly, Reeves, Ruth, Hassabis, Demis, King, Dominic, Suleyman, Mustafa, Back, Trevor, Nielson, Christopher, Ledsam, Joseph R., Mohamed, Shakir·2019·Nature

DOI 10.1038/s41586-019-1390-1PMC PMC6722431

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Abstract

The early prediction of deterioration could have an important role in supporting healthcare professionals, as an estimated 11% of deaths in hospital follow a failure to promptly recognize and treat deteriorating patients 1. To achieve this goal requires predictions of patient risk that are continuously updated and accurate, and delivered at an individual level with sufficient context and enough time to act. Here we develop a deep learning approach for the continuous risk prediction of future deterioration in patients, building on recent work that models adverse events from electronic health records 2-17 and using acute kidney injury-a common and potentially life-threatening condition 18-as an exemplar. Our model was developed on a large, longitudinal dataset of electronic health records that cover diverse clinical environments, comprising 703,782 adult patients across 172 inpatient and 1,062 outpatient sites. Our model predicts 55.8% of all inpatient episodes of acute kidney injury, and 90.2% of all acute kidney injuries that required subsequent administration of dialysis, with a lead time of up to 48 h and a ratio of 2 false alerts for every true alert. In addition to predicting future acute kidney injury, our model provides confidence assessments and a list of the clinical features that are most salient to each prediction, alongside predicted future trajectories for clinically relevant blood tests 9. Although the recognition and prompt treatment of acute kidney injury is known to be challenging, our approach may offer opportunities for identifying patients at risk within a time window that enables early treatment.

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Authors: Tomašev, N., Glorot, X., Rae, J. W., Zielinski, M., Askham, H., Saraiva, A., Mottram, A., Meyer, C., Ravuri, S., Protsyuk, I., Connell, A., Hughes, C. O., Karthikesalingam, A., Cornebise, J., Montgomery, H., Rees, G., Laing, C., Baker, C. R., Peterson, K., Reeves, R., Hassabis, D., King, D., Suleyman, M., Back, T., Nielson, C., Ledsam, J. R., & Mohamed, S.
Affiliations: Google DeepMind (United Kingdom) (GB)
University College London (GB)
University College Hospital (GB)
United States Department of Veterans Affairs (US)
University of Utah (US)
University of Nevada, Reno (US)
Corresponding author: Tomašev, Nenad
Ledsam, Joseph R.
ORCID: Tomašev, Nenad
Glorot, Xavier
Askham, Harry
Meyer, Clemens
Ravuri, Suman
Connell, Alistair
Hughes, Cían O.
Karthikesalingam, Alan
Cornebise, Julien
Montgomery, Hugh
Rees, Geraint
Peterson, Kelly
Reeves, Ruth
Hassabis, Demis
King, Dominic
Suleyman, Mustafa
Back, Trevor
Ledsam, Joseph R.
Mohamed, Shakir
Venue: Nature
Publisher: Springer Science and Business Media LLC
Published: 2019-07-31
Volume: 572
Issue: 7767
Pages: 116-119
ISSN: 0028-0836 (ISSN-L)
1476-4687
DOI: 10.1038/s41586-019-1390-1
PMC: PMC6722431
OpenAlex: W2964696298
Cited by: 1,086
References: 60
Citations by year: 2026: 123 · 2025: 149 · 2024: 122 · 2023: 150 · 2022: 154 · 2021: 169 · 2020: 180 · 2019: 39
FWCI: 61.95
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Nature (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
RePEc: Research Papers in Economics (submittedVersion)
UCL Discovery (University College London) (submittedVersion)
Funders: National Institutes of Health (VA999999)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Health Sciences › Medicine › Nephrology › Acute Kidney Injury Research
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords: acute kidney injury, deep learning, electronic health records, clinical deterioration, dialysis requirement, confidence estimation, false alarms, clinical decision support
MeSH terms: Adolescent, Adult, Aged, Aged, 80 and over, Computer Simulation, False Positive Reactions, Female, Humans, Male, Middle Aged, ROC Curve, Risk Assessment, Clinical Laboratory Techniques, Pulmonary Disease, Chronic Obstructive, Uncertainty, Young Adult, Acute Kidney Injury, Datasets as Topic

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A clinically applicable approach to continuous prediction of future acute kidney injury

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