Institute of Clinical Pharmacology, Goethe - University, Frankfurt am Main, Germany.
Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Frankfurt am Main, Germany.
Eur J Pain. 2021 Feb;25(2):442-465. doi: 10.1002/ejp.1683. Epub 2020 Nov 3.
In pain research and clinics, it is common practice to subgroup subjects according to shared pain characteristics. This is often achieved by computer-aided clustering. In response to a recent EU recommendation that computer-aided decision making should be transparent, we propose an approach that uses machine learning to provide (1) an understandable interpretation of a cluster structure to (2) enable a transparent decision process about why a person concerned is placed in a particular cluster.
Comprehensibility was achieved by transforming the interpretation problem into a classification problem: A sub-symbolic algorithm was used to estimate the importance of each pain measure for cluster assignment, followed by an item categorization technique to select the relevant variables. Subsequently, a symbolic algorithm as explainable artificial intelligence (XAI) provided understandable rules of cluster assignment. The approach was tested using 100-fold cross-validation.
The importance of the variables of the data set (6 pain-related characteristics of 82 healthy subjects) changed with the clustering scenarios. The highest median accuracy was achieved by sub-symbolic classifiers. A generalized post-hoc interpretation of clustering strategies of the model led to a loss of median accuracy. XAI models were able to interpret the cluster structure almost as correctly, but with a slight loss of accuracy.
Assessing the variables importance in clustering is important for understanding any cluster structure. XAI models are able to provide a human-understandable interpretation of the cluster structure. Model selection must be adapted individually to the clustering problem. The advantage of comprehensibility comes at an expense of accuracy.
在疼痛研究和临床实践中，根据共同的疼痛特征对受试者进行分组是一种常见做法。这通常通过计算机辅助聚类来实现。针对最近欧盟关于计算机辅助决策应该透明的建议，我们提出了一种使用机器学习的方法，该方法提供了（1）对聚类结构的可理解解释，以（2）使有关人员被分配到特定聚类的决策过程透明。
可理解性是通过将解释问题转化为分类问题来实现的：使用子符号算法来估计每个疼痛测量对聚类分配的重要性，然后使用项目分类技术选择相关变量。随后，符号算法作为可解释的人工智能 (XAI) 提供了可理解的聚类分配规则。该方法使用 100 倍交叉验证进行了测试。
数据集的变量（82 位健康受试者的 6 个疼痛相关特征）的重要性随着聚类场景的变化而变化。子符号分类器的最高中位数准确率。对模型聚类策略的广义事后解释导致中位数准确率降低。XAI 模型能够几乎正确地解释聚类结构，但准确性略有下降。
评估聚类中变量的重要性对于理解任何聚类结构都很重要。XAI 模型能够提供对聚类结构的人类可理解的解释。模型选择必须根据聚类问题进行个性化调整。可理解性的优势是以准确性为代价的。
