利用多重简单形约束优化程序（MSiCOR）对带有协变量的混合马尔可夫链进行序列数据聚类
Clustering sequence data with mixture Markov chains with covariates using multiple simplex constrained optimization routine (MSiCOR)
AI总结：
本文提出一种基于模式搜索的全局优化方法，用于在简单形集合上优化目标函数，从而改进混合马尔可夫模型（MMM）参数估计。该方法在MS患者治疗序列聚类中表现出色，并结合协变量进行患者分群。
摘要：
混合马尔可夫模型（MMM）是一种广泛用于聚类来自有限状态空间的事件序列的工具。然而，由于MMM似然函数多模态，其最大化仍具挑战性。尽管期望-最大化（EM）算法仍是估计MMM参数最流行的方法之一，但EM算法的收敛性并不总是保证。鉴于在受约束参数空间上最大化混合似然函数的计算挑战，我们开发了一种基于模式搜索的全局优化技术，可用于在简单形集合上优化任意目标函数，最终用于最大化MMM似然函数。这被证明优于其他相关全局优化技术。在模拟实验中，所提出的方法在MMM参数估计性能方面优于期望最大化（EM）算法。所提出的方法应用于根据多发性硬化症（MS）患者的疾病修改治疗（DMTs）治疗序列进行聚类。我们还提出了一种新的方法，利用带有协变量的MMM对MS患者进行聚类，基于DMTs处方和相关临床特征（协变量）。根据分析，我们将MS患者分为3个聚类。进一步的聚类特定相关协变量总结表明，各聚类间存在患者差异。
英文摘要：
Mixture Markov Model (MMM) is a widely used tool to cluster sequences of events coming from a finite state-space. However the MMM likelihood being multi-modal, the challenge remains in its maximization. Although Expectation-Maximization (EM) algorithm remains one of the most popular ways to estimate the MMM parameters, however convergence of EM algorithm is not always guaranteed. Given the computational challenges in maximizing the mixture likelihood on the constrained parameter space, we develop a pattern search-based global optimization technique which can optimize any objective function on a collection of simplexes, which is eventually used to maximize MMM likelihood. This is shown to outperform other related global optimization techniques. In simulation experiments, the proposed method is shown to outperform the expectation-maximization (EM) algorithm in the context of MMM estimation performance. The proposed method is applied to cluster Multiple sclerosis (MS) patients based on their treatment sequences of disease-modifying therapies (DMTs). We also propose a novel method to cluster people with MS based on DMT prescriptions and associated clinical features (covariates) using MMM with covariates. Based on the analysis, we divided MS patients into 3 clusters. Further cluster-specific summaries of relevant covariates indicate patient differences among the clusters.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
