基于热带几何的可解释机器学习方法在晚期心力衰竭预后中的应用
A Novel Tropical Geometry-based Interpretable Machine Learning Method: Application in Prognosis of Advanced Heart Failure
摘要：
一个模型的可解释性是必不可少的许多实际应用，如临床决策支持系统。本文提出了一种新的可解释的机器学习方法，该方法能够以人类可理解的规则来建模输入变量和响应之间的关系。该方法将热带几何应用于模糊推理系统，通过监督学习发现变量编码函数和显著规则。利用人工数据集进行实验，研究了该算法在分类和规则发现方面的性能和容量。此外，将所提出的方法应用于临床应用，该临床应用确定了将受益于先进疗法（例如心脏移植或持久的机械循环支持）的心力衰竭患者。实验结果表明，该网络取得了很好的性能分类任务。除了从数据集中学习人类可理解的规则外，现有的模糊领域知识可以很容易地转移到网络中，并用于促进模型训练。从我们的结果来看，所提出的模型和学习现有领域知识的能力可以显着提高模型的泛化能力。所提出的网络的特点，使其有希望在应用程序中需要模型的可靠性和理由。
英文摘要：
A model's interpretability is essential to many practical applications such as clinical decision support systems. In this paper, a novel interpretable machine learning method is presented, which can model the relationship between input variables and responses in humanly understandable rules. The method is built by applying tropical geometry to fuzzy inference systems, wherein variable encoding functions and salient rules can be discovered by supervised learning. Experiments using synthetic datasets were conducted to investigate the performance and capacity of the proposed algorithm in classification and rule discovery. Furthermore, the proposed method was applied to a clinical application that identified heart failure patients that would benefit from advanced therapies such as heart transplant or durable mechanical circulatory support. Experimental results show that the proposed network achieved great performance on the classification tasks. In addition to learning humanly understandable rules from the dataset, existing fuzzy domain knowledge can be easily transferred into the network and used to facilitate model training. From our results, the proposed model and the ability of learning existing domain knowledge can significantly improve the model generalizability. The characteristics of the proposed network make it promising in applications requiring model reliability and justification.
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。
