A particular challenge for disease progression modeling is the heterogeneity of a disease and its manifestations in the patients. Existing approaches often assume the presence of a single disease progression characteristics which is unlikely for neurodegenerative disorders such as Parkinson's disease. In this paper, we propose a hierarchical time-series model that can discover multiple disease progression dynamics. The proposed model is an extension of an input-output hidden Markov model that takes into account the clinical assessments of patients' health status and prescribed medications. We illustrate the benefits of our model using a synthetically generated dataset and a real-world longitudinal dataset for Parkinson's disease.

翻译：疾病发展模型的一个特殊挑战是疾病的异质性及其在患者中的表现形式,现有方法往往假设存在单一的疾病发展特征,而这种特征对于帕金森氏病等神经退化性疾病来说是不大可能的。在本文件中,我们提出了一个可发现多种疾病发展动态的等级时间序列模型。拟议的模型是一个输入-输出隐藏的马尔科夫模型的延伸,该模型考虑到对病人健康状况和处方药物的临床评估。我们用合成生成的数据集和帕金森病真实世界纵向数据集来说明我们的模型的好处。