Electronic health records contain valuable information for monitoring patients' health trajectories over time. Disease progression models have been developed to understand the underlying patterns and dynamics of diseases using these data as sequences. However, analyzing temporal data from EHRs is challenging due to the variability and irregularities present in medical records. We propose a Markovian generative model of treatments developed to (i) model the irregular time intervals between medical events; (ii) classify treatments into subtypes based on the patient sequence of medical events and the time intervals between them; and (iii) segment treatments into subsequences of disease progression patterns. We assume that sequences have an associated structure of latent variables: a latent class representing the different subtypes of treatments; and a set of latent stages indicating the phase of progression of the treatments. We use the Expectation-Maximization algorithm to learn the model, which is efficiently solved with a dynamic programming-based method. Various parametric models have been employed to model the time intervals between medical events during the learning process, including the geometric, exponential, and Weibull distributions. The results demonstrate the effectiveness of our model in recovering the underlying model from data and accurately modeling the irregular time intervals between medical actions.

翻译：电子健康记录包含监测患者随时间健康轨迹的宝贵信息。疾病进展模型已被开发用于利用这些序列数据理解疾病的潜在模式与动态。然而，由于医疗记录中存在的变异性和不规则性，分析电子健康记录的时间数据具有挑战性。我们提出了一种马尔可夫生成式治疗模型，旨在：(i) 建模医疗事件之间的不规则时间间隔；(ii) 基于患者医疗事件序列及时间间隔将治疗划分为亚型；(iii) 将治疗分割为疾病进展模式的子序列。我们假设序列具有关联的潜在变量结构：表示不同治疗亚型的潜在类别，以及指示治疗进展阶段的一组潜在阶段。我们使用期望最大化算法学习该模型，并通过基于动态规划的方法高效求解。在学习过程中，我们采用多种参数模型（包括几何分布、指数分布和韦布尔分布）对医疗事件间的时间间隔进行建模。结果表明，我们的模型能够有效从数据中恢复潜在模型，并准确建模医疗行为之间的不规则时间间隔。