A binary hidden Markov model on spatial network for amyotrophic lateral sclerosis disease spreading pattern analysis
A binary hidden Markov model on spatial network for amyotrophic lateral sclerosis disease spreading pattern analysis
复制标题
DOI：
10.1002/sim.8956
复制
发表时间：
2021-03
影响因子：
2
通讯作者：
Yei Eun Shin;Dawei Liu;H. Sang;T. Ferguson;P. Song
中科院分区：
文献类型：
--
作者：
Yei Eun Shin;Dawei Liu;H. Sang;T. Ferguson;P. Song
肌萎缩侧索硬化症(ALS)是一种神经系统疾病，始于一个焦点，然后逐渐扩散到神经系统的其他部分。肌萎缩侧索硬化症的主要临床症状之一是肌肉无力。为了研究肌肉无力的传播模式，我们分析了时空二进制肌肉力量数据，这些数据表明观察到的肌肉力量是受损的还是健康的。我们提出了一种基于隐马尔可夫模型的方法，该方法假设观察到的疾病状态取决于两个潜在的疾病状态。该模型使我们能够估计ALS疾病的发病率和疾病状态转变的概率。具体地说，后者是由Logistic自回归建模的，因为易感肌肉的空间网络遵循马尔科夫过程。建议的模型是灵活的，允许将历史肌肉状况及其空间关系包括在分析中。为了估计模型参数，我们提出了一种带有偏差校正的最大化稀疏惩罚似然的迭代算法，并使用维特比算法来标记隐藏的疾病状态。我们应用所提出的方法来分析来自EMPOWER研究的ALS患者的数据。
Amyotrophic lateral sclerosis (ALS) is a neurological disease that starts at a focal point and gradually spreads to other parts of the nervous system. One of the main clinical symptoms of ALS is muscle weakness. To study spreading patterns of muscle weakness, we analyze spatiotemporal binary muscle strength data, which indicates whether observed muscle strengths are impaired or healthy. We propose a hidden Markov model‐based approach that assumes the observed disease status depends on two latent disease states. The model enables us to estimate the incidence rate of ALS disease and the probability of disease state transition. Specifically, the latter is modeled by a logistic autoregression in that the spatial network of susceptible muscles follows a Markov process. The proposed model is flexible to allow both historical muscle conditions and their spatial relationships to be included in the analysis. To estimate the model parameters, we provide an iterative algorithm to maximize sparse‐penalized likelihood with bias correction, and use the Viterbi algorithm to label hidden disease states. We apply the proposed approach to analyze the ALS patients' data from EMPOWER Study.