基于医学索赔数据建模疾病动态的马尔科夫调节标记的波松过程
Sina Mews1, Bastian Surmann2, Lena Hasemann2
1Department of Business Administration and Economics, Bielefeld University, Bielefeld, Germany.
Statistics in medicine
|June 12, 2023
概括
马尔科夫调制标记的波桑过程 (MMMPPs) 模型患者疾病动态使用信息化的医疗保健声明. 这种方法揭示了不同的医疗保健利用模式和疾病进展的个体差异.
科学领域:
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
- 流行病学 流行病学
背景情况:
- 医疗声明数据包含有信息的,非随机的观察结果,反映了患者的基本健康状况.
- 建模疾病动态需要考虑医疗互动的时间和内容的方法.
研究的目的:
- 引入马尔科夫调制的标记式Poisson过程 (MMMPPs) 用于利用索赔数据建模患者疾病轨迹.
- 共同建模依赖潜伏疾病状态的事件时间和特定事件信息 (标记).
主要方法:
- 使用MMMPPs开发了一个框架,其中连续时间的马尔科夫链控制了医疗保健相互作用的速度.
- 模拟观察过程 (事件时间) 和标记过程 (事件数据) 作为状态依赖.
- 将MMMPP模型应用于慢性阻塞性肺病 (COPD) 索赔数据,分析药物使用和咨询间隔.
主要成果:
- MMMPP有效地模拟了医疗保健索赔数据的信息性质.
- 该模型确定了与COPD患者的疾病过程相关的医疗保健利用的独特模式.
- 揭示了疾病状态动态和转变的个体间差异.
结论:
- MMMPPs提供了一个强大的统计框架,用于从纵向医疗保健索赔分析复杂疾病动态.
- 这种方法提高了对患者健康轨迹和寻求医疗保健行为的理解.
- 该方法为个性化医学和公共卫生研究提供了宝贵的见解.
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