In this study, we present a novel clinical decision support system and
discuss its interpretability-related properties. It combines a decision set of
rules with a machine learning scheme to offer global and local
interpretability. More specifically, machine learning is used to predict the
likelihood of each of those rules to be correct for a particular patient, which
may also contribute to better predictive performances. Moreover, the
reliability analysis of individual predictions is also addressed, contributing
to further personalized interpretability. The combination of these several
elements may be crucial to obtain the clinical stakeholders' trust, leading to
a better assessment of patients' conditions and improvement of the physicians'
decision-making.

本研究提出了一种新型的临床决策支持系统，结合了一组规则和机器学习算法，提供了全局和局部的可解释性，并使用机器学习算法预测每个规则在特定患者的正确性可能性，以更好地提高预测性能，并提高个性化解释能力，这些元素的结合可以帮助医生更好地评估患者的情况，提高医生的决策能力。