IEEE J Biomed Health Inform. 2020 Feb;24(2):486-492. doi: 10.1109/JBHI.2019.2916667. Epub 2019 May 13.
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.
估算患者的医院死亡率对于协助临床医生做出决策和医院提供者分配资源非常重要。本文提出了一种带有注意力机制的多任务循环神经网络，通过重建患者的生理时间序列作为辅助任务来预测患者的医院死亡率。实验在一个大型公共电子健康记录数据库 MIMIC-III 上进行。使用前 24 小时重症监护期间的 15 个生理测量值来预测出院前的死亡。与传统的简化急性生理学评分（SAPS-II）相比，当基于相同的 24 小时观察期进行预测时，所提出的多任务学习模型具有更好的灵敏度（0.503 ± 0.020 对 0.365 ± 0.021）。建议每天更新多任务学习模型，至少有 6 小时的观察期，以便其表现与 SAPS-II 相似或更好。将来，可以考虑将干预需求作为另一个任务，以进一步优化多任务学习模型的性能。
