Estimating hospital mortality of patients is important in assisting clinicians to make decisions and hospital providers to allocate resources. This paper proposed a multi-task recurrent neural network with attention mechanisms to predict patients’ hospital mortality, using reconstruction of patients’ physiological time series as an auxiliary task. Experiments were conducted on a large public electronic health record database, i.e., MIMIC-III. Fifteen physiological measurements during the first 24 h of critical care were used to predict death before hospital discharge. Compared with the conventional simplified acute physiology score (SAPS-II), the proposed multi-task learning model achieved better sensitivity (0.503 ± 0.020 versus 0.365 ± 0.021), when predictions were made based on the same 24-h observation period. The multi-task learning model is recommended to be updated daily with at least a 6-h observation period, in order for it to perform similarly or better than the SAPS-II. In the future, the need for intervention can be considered as another task to further optimize the performance of the multi-task learning model.

中文翻译：

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使用具有注意机制的多任务递归神经网络预测患者的医院死亡率

估计患者的医院死亡率对于协助临床医生做出决定以及帮助医院提供者分配资源很重要。本文提出了一种具有注意机制的多任务递归神经网络，以重建患者的生理时间序列为辅助任务，以预测患者的医院死亡率。实验是在大型公共电子健康记录数据库MIMIC-III上进行的。重症监护的头24小时内进行了15次生理测量，以预测出院前的死亡。与传统的简化急性生理学评分（SAPS-II）相比，当在相同的24小时观察期内进行预测时，所提出的多任务学习模型具有更好的敏感性（0.503±0.020对0.365±0.021）。建议多任务学习模型每天至少观察6小时，以使其性能与SAPS-II相似或更好。将来，干预的需求可以被认为是进一步优化多任务学习模型性能的另一项任务。