论文标题
聚类方法和贝叶斯推断以分析免疫疾病的演变
Clustering methods and Bayesian inference for the analysis of the evolution of immune disorders
论文作者
论文摘要
根据所研究的问题，选择适当的超参数用于无监督聚类算法是一项漫长的挑战，我们在调整聚类算法的同时应对免疫疾病诊断。我们通过分析来自全身性红斑狼疮患者的实验室数据记录了具有不同参数选择不同的超级参数选择的实验室数据，比较了无监督聚类算法检测疾病耀斑和缓解周期的潜在能力。为了确定哪种聚类策略是基于应用于排名的Plackett-luce模型进行贝叶斯分析的最佳策略。该分析量化了给定问题的聚类方法的不确定性
Choosing appropriate hyperparameters for unsupervised clustering algorithms in an optimal way depending on the problem under study is a long standing challenge, which we tackle while adapting clustering algorithms for immune disorder diagnoses. We compare the potential ability of unsupervised clustering algorithms to detect disease flares and remission periods through analysis of laboratory data from systemic lupus erythematosus patients records with different hyperparameter choices. To determine which clustering strategy is the best one we resort to a Bayesian analysis based on the Plackett-Luce model applied to rankings. This analysis quantifies the uncertainty in the choice of clustering methods for a given problem
