Preeclampsia is a heterogeneous and complex disease associated with rising morbidity and mortality in pregnant women and newborns in the US. Early recognition of patients at risk is a pressing clinical need to reduce the risk of adverse outcomes. We assessed whether information routinely collected in electronic medical records (EMR) could enhance the prediction of preeclampsia risk beyond what is achieved in standard of care assessments. We developed a digital phenotyping algorithm to curate 108,557 pregnancies from EMRs across the Mount Sinai Health System, accurately reconstructing pregnancy journeys and normalizing these journeys across different hospital EMR systems. We then applied machine learning approaches to a training dataset (N = 60,879) to construct predictive models of preeclampsia across three major pregnancy time periods (ante-, intra-, and postpartum). The resulting models predicted preeclampsia with high accuracy across the different pregnancy periods, with areas under the receiver operating characteristic curves (AUC) of 0.92, 0.82, and 0.89 at 37 gestational weeks, intrapartum and postpartum, respectively. We observed comparable performance in two independent patient cohorts. While our machine learning approach identified known risk factors of preeclampsia (such as blood pressure, weight, and maternal age), it also identified other potential risk factors, such as complete blood count related characteristics for the antepartum period. Our model not only has utility for earlier identification of patients at risk for preeclampsia, but given the prediction accuracy exceeds what is currently achieved in clinical practice, our model provides a path for promoting personalized precision therapeutic strategies for patients at risk.

先兆子痫是一种异质且复杂的疾病，与美国孕妇和新生儿的发病率和死亡率上升有关。早期识别有风险的患者是降低不良后果风险的迫切临床需求。我们评估了电子病历 (EMR) 中常规收集的信息是否可以增强对先兆子痫风险的预测，超出护理评估标准。我们开发了一种数字表型算法来管理西奈山卫生系统中的 108,557 例 EMR 妊娠，准确地重建妊娠旅程并在不同医院 EMR 系统中规范这些旅程。然后我们将机器学习方法应用于训练数据集（N = 60,879) 来构建三个主要妊娠时期（产前、产中和产后）的先兆子痫预测模型。由此产生的模型在不同妊娠期以高精度预测先兆子痫，在妊娠 37 周、产时和产后的受试者工作特征曲线 (AUC) 下面积分别为 0.92、0.82 和 0.89。我们在两个独立的患者队列中观察到了可比的表现。虽然我们的机器学习方法确定了先兆子痫的已知风险因素（如血压、体重和母亲年龄），但它还确定了其他潜在风险因素，如产前全血细胞计数相关特征。我们的模型不仅可用于早期识别有先兆子痫风险的患者，