Tomašev, N;
Harris, N;
Baur, S;
Mottram, A;
Glorot, X;
Rae, JW;
Zielinski, M;
... Mohamed, S; + view all
(2021)
Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records.
Nature Protocols
, 16
pp. 2765-2787.
10.1038/s41596-021-00513-5.
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Abstract
Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electronic health record (EHR) data. The protocol comprises five main stages: formal problem definition, data pre-processing, architecture selection, calibration and uncertainty, and generalizability evaluation. We have applied the workflow to four endpoints (acute kidney injury, mortality, length of stay and 30-day hospital readmission). The workflow can enable continuous (e.g., triggered every 6 h) and static (e.g., triggered at 24 h after admission) predictions. We also provide an open-source codebase that illustrates some key principles in EHR modeling. This protocol can be used by interdisciplinary teams with programming and clinical expertise to build deep-learning prediction models with alternate data sources and prediction tasks.
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