Assessment of physiological instability preceding adverse events on hospital wards has been previously investigated through clinical early warning score systems. Early warning scores are simple to use yet they consider data as independent and identically distributed random variables. Deep learning applications are able to learn from sequential data, however they lack interpretability and are thus difficult to deploy in clinical settings. We propose the ‘Deep Early Warning System’ (DEWS), an interpretable end-to-end deep learning model that interpolates temporal data and predicts the probability of an adverse event, defined as the composite outcome of cardiac arrest, mortality or unplanned ICU admission. The model was developed and validated using routinely collected vital signs of patients admitted to the the Oxford University Hospitals between 21st March 2014 and 31st March 2018. We extracted 45 314 vital-sign measurements as a balanced training set and 359 481 vital-sign measurements as an imbalanced testing set to mimic a real-life setting of emergency admissions. DEWS achieved superior accuracy than the state-of-the-art that is currently implemented in clinical settings, the National Early Warning Score, in terms of the overall area under the receiver operating characteristic curve (AUROC) (0.880 vs. 0.866) and when evaluated independently for each of the three outcomes. Our attention-based architecture was able to recognize ‘historical’ trends in the data that are most correlated with the predicted probability. With high sensitivity, improved clinical utility and increased interpretability, our model can be easily deployed in clinical settings to supplement existing EWS systems.

中文翻译：

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用于临床恶化检测的可解释性深层预警系统

先前已经通过临床预警评分系统对医院病房发生不良事件之前的生理不稳定性进行了评估。预警分数易于使用，但它们将数据视为独立且分布均匀的随机变量。深度学习应用程序能够从顺序数据中学习，但是它们缺乏可解释性，因此难以在临床环境中进行部署。我们提出了“深度预警系统”（DEWS），这是一种可解释的端到端深度学习模型，可对时间数据进行插值并预测不良事件的可能性，定义为心脏骤停，死亡率或计划外ICU入院的综合结果。该模型是根据2014年3月21日至2018年3月31日在牛津大学医院住院的患者的常规生命体征收集和开发的。我们提取了45 314个生命体征测量值作为平衡训练集，并提取359 481生命体征测量值作为平衡训练集。一种不平衡的测试集，以模仿现实生活中的紧急情况录取。就接收器工作特征曲线（AUROC）下的总面积（0.880与0.866）以及何时对三个结局中的每个结局进行独立评估。我们基于注意力的体系结构能够识别与预测概率最相关的数据“历史”趋势。高灵敏度