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Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records
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Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records
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Journal Article
Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records
2021
Overview
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.
We present a practical workflow describing how to use deep learning to develop continuous-risk models that can predict various adverse outcomes from structured electronic health records.
Publisher
Nature Publishing Group UK,Nature Publishing Group
Subject
