Deep dynamic imputation of clinical time series for mortality prediction
Deep dynamic imputation of clinical time series for mortality prediction
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DOI：
10.1016/j.ins.2021.08.016
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发表时间：
2021-08-20
影响因子：
8.1
通讯作者：
Li, Xue
中科院分区：
文献类型：
--
作者：
Shi, Zhenkun;Wang, Sen;Li, Xue
临床时间序列数据的缺失值是普遍和不可避免的；它们不仅增加了分析数据的复杂性和难度，而且还会导致有偏差的结果。为了解决这两个问题，研究人员一直在探索基于循环神经网络（RNN）的方法，以检测缺失值的处理程度，以达到最先进的性能。然而，这些方法有两个实际的缺点。1)处理具有多个不规则、异常值的时间序列数据是困难的。2)缺失的临床资料中可能存在的模式没有被充分考虑。此外，据我们所知，这些方法都没有被明确地设计为动态优化在临床时间序列分析领域的更好表现的插补质量。在考虑输入值质量的基础上，提出了一种基于门控循环单元（gru）的医学预测两步集成输入-预测模型。在第一步中，使用基于具有隐藏状态衰减机制的补充GRU （RGRU-D）的复杂模型来输入缺失值，然后通过两个附加层进行评估。第二步，利用优化后的估算值预测危重患者的死亡风险。我们的模型有效地为集成深度体系结构中的掩蔽、时间间隔、突发和累积缺失率变量提供缺失值。在真实ICU数据集上的大量实验表明，我们的模型在输入质量和预测精度方面优于所比较的方法。(c) 2021爱思唯尔公司版权所有。
Missing values in clinical time-series data are pervasive and inevitable; they not only increase the complexity and difficulty of analyzing the data but also lead to biased results. To tackle these two problems, researchers have been exploring recurrent neural network (RNN)-based methods for detecting how well missing values are addressed with the aim of achieving state-of-the-art performance. However, these methods have two practical drawbacks. 1) Handling time-series data with multiple, irregular, abnormal values is difficult. 2) The patterns that may be present in the missing clinical data are not thoroughly considered. Moreover, to the best of our knowledge, none of these methods have been explicitly designed to dynamically optimize the imputation quality for better performance in the realm of clinical time-series analytics. By considering the quality of imputed values, we propose a 2-step integrated imputation-prediction model based on gated recurrent units (GRUs) for medical prediction tasks. In the first step, the missing values are imputed using a sophisticated model based on a replenished GRU with a hidden state decay mechanism (RGRU-D), which is followed by evaluation through two additional layers. In the second step, the optimized imputed values are used to predict the risk of mortality in critical patients. Our model effectively supplies missing values for the masking, time interval, bursty, and cumulative missing rate variables within an integrated deep architecture. Extensive experiments on a real-world ICU dataset demonstrate that our model performs better than the compared methods in terms of the imputation quality and prediction accuracy. (c) 2021 Elsevier Inc. All rights reserved.