ITMO University, Saint Petersburg, Russia.
Almazov National Medical Research Center, Saint Petersburg, Russia.
Stud Health Technol Inform. 2021 Oct 27;285:100-105. doi: 10.3233/SHTI210580.
The relevance of this study lies in improvement of machine learning models understanding. We present a method for interpreting clustering results and apply it to the case of clinical pathways modeling. This method is based on statistical inference and allows to get the description of the clusters, determining the influence of a particular feature on the difference between them. Based on the proposed approach, it is possible to determine the characteristic features for each cluster. Finally, we compare the method with the Bayesian inference explanation and with the interpretation of medical experts [1].
本研究的相关性在于改进机器学习模型的理解。我们提出了一种解释聚类结果的方法，并将其应用于临床路径建模的情况。该方法基于统计推断，可以得到聚类的描述，确定特定特征对它们之间差异的影响。基于所提出的方法，可以确定每个聚类的特征。最后，我们将该方法与贝叶斯推理解释以及医学专家的解释进行了比较[1]。
