论文标题
基于医学索赔数据，Markov修改了用于建模疾病动态的泊松过程
Markov-modulated marked Poisson processes for modelling disease dynamics based on medical claims data
论文作者
论文摘要
我们探讨了马尔可夫修饰的标记泊松过程（MMMPPS），作为基于医疗索赔数据随时间的自然框架，用于对患者的疾病动态进行建模。在索赔数据中，观察不仅发生在随机点上，而且还具有信息性，即受未观察到的疾病水平驱动，因为较差的健康状况通常会导致与医疗保健系统的频繁相互作用。因此，我们将观察过程建模为Markov修饰的泊松过程，其中医疗保健相互作用的速率受连续时间马尔可夫链的控制。它的状态是患者潜在疾病水平的代理，并进一步确定每个观察时间收集的其他数据的分布，即所谓的标记。总体而言，MMMPP通过构成两个依赖状态的过程（与事件时间）和标记过程（对应于事件特定信息）的观察过程（对应于事件时间），共同模拟观测值及其信息时间点，这两者都取决于基本状态。使用诊断为慢性阻塞性肺疾病（COPD）的患者的索赔数据，通过对连续医师咨询之间的间隔长度进行建模，来说明该方法。结果表明，MMMPP能够检测与疾病过程有关的不同医疗保健利用模式，并揭示了状态转换动力学的个体间差异。
We explore Markov-modulated marked Poisson processes (MMMPPs) as a natural framework for modelling patients' disease dynamics over time based on medical claims data. In claims data, observations do not only occur at random points in time but are also informative, i.e. driven by unobserved disease levels, as poor health conditions usually lead to more frequent interactions with the healthcare system. Therefore, we model the observation process as a Markov-modulated Poisson process, where the rate of healthcare interactions is governed by a continuous-time Markov chain. Its states serve as proxies for the patients' latent disease levels and further determine the distribution of additional data collected at each observation time, the so-called marks. Overall, MMMPPs jointly model observations and their informative time points by comprising two state-dependent processes: the observation process (corresponding to the event times) and the mark process (corresponding to event-specific information), which both depend on the underlying states. The approach is illustrated using claims data from patients diagnosed with chronic obstructive pulmonary disease (COPD) by modelling their drug use and the interval lengths between consecutive physician consultations. The results indicate that MMMPPs are able to detect distinct patterns of healthcare utilisation related to disease processes and reveal inter-individual differences in the state-switching dynamics.
