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A clinically applicable approach to continuous prediction of future acute kidney injury
Open accessAbstract
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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“Many research groups have tackled early prediction of AKI using electronic health records (EHR) data [8–11], but no model so far explains the rationale behind specific predictions despite a clear need for explainable and actionable predictions [6, 12].”
From Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care (2021) · cites this paper as [9]
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“it was predominantly made up of data from older non-Black men; the average age was 62 years old and over 90% were males.”
From Bias in artificial intelligence algorithms and recommendations for mitigation (2023) · cites this paper as [27]
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“An example of this includes an algorithm developed to predict acute kidney injury (AKI) using clinical data from the US Department of Veteran Affairs [27].”
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“Though the dataset used to develop the algorithm was large and diverse, containing data from over 700,000 patients from multiple centers”
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“Models that use EHR, laboratory, and physiological data predict outcomes such as acute kidney injury [14] and sepsis or critical-care deterioration [15,16].”
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“Model achieves a higher ROC AUC in shorter time windows.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [80]
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“The model achieves a lower PR AUC in shorter time windows.”
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Machine Learning for Predicting Rare Clinical Outcomes—Finding Needles in a Haystack
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Clinical Decision Support for Laboratory Testing
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A time-aware attention model for prediction of acute kidney injury after pediatric cardiac surgery
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Metadata
- 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
- Venue
- Nature
- Published
- 2019-07-31
- Volume
- 572
- Issue
- 7767
- Pages
- 116-119
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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