In a general inpatient population, we predicted patient‐specific medication orders based on structured information in the electronic health record (EHR). Data on over three million medication orders from an academic medical center were used to train two machine‐learning models: A deep learning sequence model and a logistic regression model. Both were compared with a baseline that ranked the most frequently ordered medications based on a patient’s discharge hospital service and amount of time since admission. Models were trained to predict from 990 possible medications at the time of order entry. Fifty‐five percent of medications ordered by physicians were ranked in the sequence model’s top‐10 predictions (logistic model: 49%) and 75% ranked in the top‐25 (logistic model: 69%). Ninety‐three percent of the sequence model’s top‐10 prediction sets contained at least one medication that physicians ordered within the next day. These findings demonstrate that medication orders can be predicted from information present in the EHR.

中文翻译：

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根据电子病历数据预测住院用药顺序。

在一般住院患者中，我们根据电子健康记录（EHR）中的结构化信息预测了特定于患者的用药顺序。来自学术医疗中心的三百万份用药订单数据用于训练两种机器学习模型：深度学习序列模型和逻辑回归模型。将两者都与基线进行比较，该基线根据患者的出院服务和入院以来的时间对排名最频繁的药物进行排名。训练模型以在输入订单时从990种可能的药物中进行预测。医师订购的药物中有55％在顺序模型的前10位预测中排名（后勤模型：49％），而75％在前25位预测中（后勤模型：69％）。序列模型的前10个预测集中的百分之九十三包含了第二天医生订购的至少一种药物。这些发现表明，可以根据EHR中的信息预测用药顺序。