论文标题
GRU-TV：用于多元临床时间序列数据的患者代表性的时间和速度感知gru
GRU-TV: Time- and velocity-aware GRU for patient representation on multivariate clinical time-series data
论文作者
论文摘要
电子健康记录（EHR）通常是高度的，异质和多模式的。此外，对临床变量的随机记录会导致较高的率和从EHR提取的多元临床时间序列数据中相邻记录之间的时间间隔不均匀。当前使用临床时间序列数据进行患者代表的工作将患者的生理状况视为偶发收集的记录所描述的离散过程。但是，患者的生理状况的变化是连续和动态过程。时间和变化速度的感知对于患者表示学习至关重要。在这项研究中，我们提出了一个时间和速度感知的封闭式复发单位模型（GRU-TV），以延时的方式学习临床多元时间序列数据的患者表示。神经普通微分方程（OD）和速度感知机制分别用于感知相邻记录和患者生理状况率的变化之间的时间间隔。我们对两个真实临床EHR数据集（Physionet2012，Mimic-III）进行的实验确定，GRU-TV是计算机辅助诊断（CAD）任务的强大模型，尤其是在具有高变化时间间隔的序列上。
Electronic health records (EHRs) are usually highly dimensional, heterogeneous, and multimodal. Besides, the random recording of clinical variables results in high missing rates and uneven time intervals between adjacent records in the multivariate clinical time-series data extracted from EHRs. Current works using clinical time-series data for patient representation regard the patients' physiological status as a discrete process described by sporadically collected records. However, changes in the patient's physiological condition are continuous and dynamic processes. The perception of time and velocity of change is crucial for patient representation learning. In this study, we propose a time- and velocity-aware gated recurrent unit model (GRU-TV) for patient representation learning of clinical multivariate time-series data in a time-continuous manner. The neural ordinary differential equations (ODEs) and velocity perception mechanism are applied to perceive the time interval between adjacent records and changing rate of the patient's physiological status, respectively. Our experiments on two real clinical EHR datasets (PhysioNet2012, MIMIC-III) establish that GRU-TV is a robust model on computer-aided diagnosis (CAD) tasks, especially on sequences with high-variance time intervals.
