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.

早期预测患者的预后对于针对预防性护理很重要。该协议描述了用于开发深度学习风险模型的实用工作流程，该模型可以从结构化电子健康记录 (EHR) 数据中预测各种临床和操作结果。该协议包括五个主要阶段：正式问题定义、数据预处理、架构选择、校准和不确定性以及通用性评估。我们已将工作流程应用于四个终点（急性肾损伤、死亡率、住院时间和 30 天再入院）。该工作流可以实现连续（例如，每 6 小时触发一次）和静态（例如，在入院后 24 小时触发）预测。我们还提供了一个开源代码库，说明了 EHR 建模中的一些关键原则。