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 patients1. 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 records2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17 and using acute kidney injury—a common and potentially life-threatening condition18—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 tests9. 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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The clinical data used for the training, validation and test sets were collected at the US Department of Veterans Affairs and transferred to a secure data centre with strict access controls in de-identified format. Data were used with both local and national permissions. It is not publicly available and restrictions apply to its use. The de-identified dataset (or a test subset) may be available from the US Department of Veterans Affairs, subject to local and national ethical approvals.
We make use of several open-source libraries to conduct our experiments: the machine learning framework TensorFlow (https://github.com/tensorflow/tensorflow) along with the TensorFlow library Sonnet (https://github.com/deepmind/sonnet), which provides implementations of individual model components58. Our experimental framework makes use of proprietary libraries and we are unable to publicly release this code. We detail the experiments and implementation details in the Methods and Supplementary Information to allow for independent replication.
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We thank the veterans and their families under the care of the US Department of Veterans Affairs. We thank A. Graves, O. Vinyals, K. Kavukcuoglu, S. Chiappa, T. Lillicrap, R. Raine, P. Keane, M. Seneviratne, A. Schlosberg, O. Ronneberger, J. De Fauw, K. Ruark, M. Jones, J. Quinn, D. Chou, C. Meaden, G. Screen, W. West, R. West, P. Sundberg and the Google AI team, J. Besley, M. Bawn, K. Ayoub and R. Ahmed. Finally, we thank the many physicians, administrators and researchers of the US Department of Veterans Affairs who worked on the data collection, and the rest of the DeepMind team for their support, ideas and encouragement. G.R. and H.M. were supported by University College London and the National Institute for Health Research (NIHR) University College London Hospitals Biomedical Research Centre. The views expressed are those of these author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.
G.R., H.M. and C.L. are paid contractors of DeepMind. The authors have no other competing interests to disclose.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Peer review information Nature thanks Lui G. Forni, Suchi Saria and Eric Topol for their contribution to the peer review of this work.
Extended data figures and tables
All electronic health record data available for each patient were structured into a sequential history for both inpatient and outpatient events in six-hourly blocks, shown here as circles. In each 24-h period, events without a recorded time were included in a fifth block. Apart from the data present at the current time step, the models optionally receive an embedding of the previous 48 h and the longer history of 6 months or 5 years.
The best performance was achieved by a multi-task deep recurrent highway network architecture on top of an L1-regularized deep residual embedding component that learns the best data representation end-to-end without pre-training.
a, b, The predictions were recalibrated using isotonic regression before (a) and after (b) calibration. Model predictions were grouped into 20 buckets, with a mean model risk prediction plotted against the percentage of positive labels in that bucket. The diagonal line demonstrates the ideal calibration. Source Data
a, For prediction of any AKI within 48 h at 33% precision, nearly half of all predictions are trailing, after the AKI has already occurred (orange bars) or early, more than 48 h prior (blue bars). The histogram shows the distribution of these trailing and early false positives for prediction. Incorrect predictions are mapped to their closest preceding or following episode of AKI (whichever is closer) if that episode occurs in an admission. For ±1 day, 15.2% of false positives correspond to observed AKI events within 1 day after the prediction (model reacted too early) and 2.9% correspond to observed AKI events within 1 day before the prediction (model reacted too late). b, Subgroup analysis for all false-positive alerts. In addition to the 49% of false-positive alerts that were made in admissions during which there was at least one episode of AKI, many of the remaining false-positive alerts were made in patients who had evidence of clinical risk factors present in their available electronic health record data. These risk factors are shown here for the proposed model that predicts any stage of AKI occurring within the next 48 h. Source Data
Supplementary Sections A-K, including Supplementary Figures 1-12 and Supplementary Tables 1-12. Supplementary Section A: Supplementary figures showing the visual examples from five systematically selected success cases and five systematically selected failure cases from the predictive model. Supplementary Section B: Supplementary analysis of the auxiliary numerical prediction tasks. Supplementary Section C: Additional analysis from an experiment into the significance of individual features in our trained models based on occlusion analysis. Supplementary Section D: Supplementary results and methods from the comparison of broad comparison of available models on the AKI prediction task. Supplementary Section E: Comparison of our models performance to baseline models trained on features that have been chosen by clinicians as being relevant for modelling kidney function. Supplementary Section F: The results of literature reviews into risk prediction of AKI and machine learning on electronic health records. Supplementary Section G: Supplementary analyses and results of individual subgroups of the patient population studied. Supplementary Section H: Supplementary analysis of the influence of data recency on model performance. Supplementary Section I: Analysis of the contribution of the aspects of our model’s design to its overall performance through an ablation study that removes specific components of the model, training it fully, and then comparing the simplified model’s PR AUC on the validation set. Supplementary Section J: Supplementary methods and results from the hyperparameter sweeps described in the Methods section. Supplementary Section K: Additional analysis from an experiment into the relationship between model confidence and prediction accuracy.
This file contains Source Data for Supplementary Figure 1.