EHRs的时间级联和结构模型用于颗粒再接纳预测
Temporal Cascade and Structural Modelling of EHRs for Granular Readmission Prediction
摘要：
通过挖掘电子健康记录（EHR）数据来预测（1）下一次入院何时发生以及（2）关于患者的下一次入院将发生什么可以提供粒度再入院预测以辅助临床决策制定。递归神经网络（RNN）和点过程模型通常用于时序数据的建模。简单的RNN模型假设医院访问序列在连续访问之间遵循严格的因果关系。然而，在现实世界中，患者可能具有多种共存的慢性医学病症，即，多时间间隔，这导致访问的级联，其中非即时历史访问可能对下一次访问最有影响。虽然点过程（例如，Hawkes过程）能够建模级联时间关系，它强烈依赖于先验生成过程假设。我们提出了一个新的模型，MEDCAS，以解决这些挑战。MEDCAS结合了基于RNN的模型和点过程的优势，通过将点过程建模访问类型和时间间隔集成到基于注意力的序列到序列学习模型中，该模型能够捕获时间级联关系。为了补充患者的短期访问序列，结构建模技术与图形为基础的方法被用来构建MEDCAS的点过程的标记。在三个真实世界的EHR数据集上进行了大量的实验，结果表明，\texttt{MEDCAS}在这两个任务中都优于最先进的模型。
英文摘要：
Predicting (1) when the next hospital admission occurs and (2) what will happen in the next admission about a patient by mining electronic health record (EHR) data can provide granular readmission predictions to assist clinical decision making. Recurrent neural network (RNN) and point process models are usually employed in modelling temporal sequential data. Simple RNN models assume that sequences of hospital visits follow strict causal dependencies between consecutive visits. However, in the real-world, a patient may have multiple co-existing chronic medical conditions, i.e., multimorbidity, which results in a cascade of visits where a non-immediate historical visit can be most influential to the next visit. Although a point process (e.g., Hawkes process) is able to model a cascade temporal relationship, it strongly relies on a prior generative process assumption. We propose a novel model, MEDCAS, to address these challenges. MEDCAS combines the strengths of RNN-based models and point processes by integrating point processes in modelling visit types and time gaps into an attention-based sequence-to-sequence learning model, which is able to capture the temporal cascade relationships. To supplement the patients with short visit sequences, a structural modelling technique with graph-based methods is used to construct the markers of the point process in MEDCAS. Extensive experiments on three real-world EHR datasets have been performed and the results demonstrate that \texttt{MEDCAS} outperforms state-of-the-art models in both tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
