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Rough set theory based prognostic classification models for hospice referral

机译：基于粗糙集理论的临终关怀转诊预后分类模型

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This paper explores and evaluates the application of classical and dominance-based rough set theory (RST) for the development of data-driven prognostic classification models for hospice referral. In this work, rough set based models are compared with other data-driven methods with respect to two factors related to clinical credibility: accuracy and accessibility. Accessibility refers to the ability of the model to provide traceable, interpretable results and use data that is relevant and simple to collect. We utilize retrospective data from 9,103 terminally ill patients to demonstrate the design and implementation RST- based models to identify potential hospice candidates. The classical rough set approach (CRSA) provides methods for knowledge acquisition, founded on the relational indiscernibility of objects in a decision table, to describe required conditions for membership in a concept class. On the other hand, the dominance-based rough set approach (DRSA) analyzes information based on the monotonic relationships between condition attributes values and their assignment to the decision class. CRSA decision rules for six-month patient survival classification were induced using the MODLEM algorithm. Dominance-based decision rules were extracted using the VC-DomLEM rule induction algorithm. The RST-based classifiers are compared with other predictive and rule based decision modeling techniques, namely logistic regression, support vector machines, random forests and C4.5. The RST-based classifiers demonstrate average AUC of 69.74 % with MODLEM and 71.73 % with VC-DomLEM, while the compared methods achieve average AUC of 74.21 % for logistic regression, 73.52 % for support vector machines, 74.59 % for random forests, and 70.88 % for C4.5. This paper contributes to the growing body of research in RST-based prognostic models. RST and its extensions posses features that enhance the accessibility of clinical decision support models. While the non-rule-based methods—logistic regression, support vector machines and random forests—were found to achieve higher AUC, the performance differential may be outweighed by the benefits of the rule-based methods, particularly in the case of VC-DomLEM. Developing prognostic models for hospice referrals is a challenging problem resulting in substandard performance for all of the evaluated classification methods.
机译：本文探讨并评估了经典和基于优势的粗糙集理论（RST）在临终关怀转诊数据驱动的预后分类模型开发中的应用。在这项工作中，就两个与临床可信度有关的因素，将基于粗糙集的模型与其他数据驱动方法进行了比较：准确性和可及性。可访问性是指模型提供可追溯的，可解释的结果并使用相关且易于收集的数据的能力。我们利用来自9,103名绝症患者的回顾性数据来演示基于RST的模型的设计和实现，以识别潜在的临终关怀候选人。经典的粗糙集方法（CRSA）提供了用于知识获取的方法，该方法基于决策表中对象的不相关性来描述概念类成员资格所需的条件。另一方面，基于优势的粗糙集方法（DRSA）基于条件属性值及其对决策类的分配之间的单调关系来分析信息。使用MODLEM算法得出了六个月患者生存期分类的CRSA决策规则。使用VC-DomLEM规则归纳算法提取基于优势的决策规则。将基于RST的分类器与其他基于预测和规则的决策建模技术进行比较，即逻辑回归，支持向量机，随机森林和C4.5。基于RST的分类器显示MODLEM的平均AUC为69.74％，VC-DomLEM的平均AUC为71.73％，而比较方法实现逻辑回归的平均AUC为74.21％，支持向量机的平均AUC为73.52％，随机森林的74.59％和70.88 C4.5为％。本文为基于RST的预后模型的研究不断发展做出了贡献。 RST及其扩展具有增强临床决策支持模型可访问性的功能。虽然发现非基于规则的方法（逻辑回归，支持向量机和随机森林）可以实现更高的AUC，但是基于规则的方法的优势可能会抵消性能差异，尤其是在VC-DomLEM的情况下。开发临终关怀转诊的预后模型是一个具有挑战性的问题，导致所有评估的分类方法的性能均达不到标准。

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