论文标题
医疗活动时间预测中电子健康记录的累积逗留时间代表
Cumulative Stay-time Representation for Electronic Health Records in Medical Event Time Prediction
论文作者
论文摘要
我们解决了从患者的电子健康记录（EHR）中预测何时发生疾病（即医疗事件时间（MET））的问题。糖尿病等非传染性疾病大会与累积健康状况高度相关，更具体地说，患者过去花费了多少时间。在从EHR中提取此类信息时，常见的时间序列表示是因为它着重于连续观察值（而不是累积信息）之间的详细依赖关系。我们为EHR提出了一种新的数据表示，称为累积停留时间表示（CTR），该数据直接模拟了这种累积健康状况。我们基于神经网络得出了可训练的CTR构造，该构建具有符合目标数据和可扩展性以处理高维EHR的灵活性。使用合成和现实世界数据集的数值实验表明，单独使用CTR可以实现高预测性能，并且在与它们结合使用时可以增强现有模型的性能。
We address the problem of predicting when a disease will develop, i.e., medical event time (MET), from a patient's electronic health record (EHR). The MET of non-communicable diseases like diabetes is highly correlated to cumulative health conditions, more specifically, how much time the patient spent with specific health conditions in the past. The common time-series representation is indirect in extracting such information from EHR because it focuses on detailed dependencies between values in successive observations, not cumulative information. We propose a novel data representation for EHR called cumulative stay-time representation (CTR), which directly models such cumulative health conditions. We derive a trainable construction of CTR based on neural networks that has the flexibility to fit the target data and scalability to handle high-dimensional EHR. Numerical experiments using synthetic and real-world datasets demonstrate that CTR alone achieves a high prediction performance, and it enhances the performance of existing models when combined with them.
