论文标题
从纵向观察数据中建模疾病进展轨迹
Modeling Disease Progression Trajectories from Longitudinal Observational Data
论文作者
论文摘要
分析疾病进展模式可以为许多慢性病的疾病过程提供有用的见解。这些分析可能有助于为预防试验招聘或为受影响者提供治疗的开发和个性化。我们使用隐藏的马尔可夫模型（HMM）学习疾病进展模式，并使用可视化方法将它们提炼成不同的轨迹。我们使用T1DI研究组的大型纵向观察数据将其应用于1型糖尿病（T1D）的结构域。我们的方法发现了与最近发表的发现证实的不同疾病进展轨迹。在本文中，我们描述了开发模型的迭代过程。这些方法也可以应用于随着时间的流逝而发展的其他慢性疾病。
Analyzing disease progression patterns can provide useful insights into the disease processes of many chronic conditions. These analyses may help inform recruitment for prevention trials or the development and personalization of treatments for those affected. We learn disease progression patterns using Hidden Markov Models (HMM) and distill them into distinct trajectories using visualization methods. We apply it to the domain of Type 1 Diabetes (T1D) using large longitudinal observational data from the T1DI study group. Our method discovers distinct disease progression trajectories that corroborate with recently published findings. In this paper, we describe the iterative process of developing the model. These methods may also be applied to other chronic conditions that evolve over time.
