The need for fully human-understandable models is increasingly being
recognised as a central theme in AI research. The acceptance of AI models to
assist in decision making in sensitive domains will grow when these models are
interpretable, and this trend towards interpretable models will be amplified by
upcoming regulations. One of the killer applications of interpretable AI is
medical practice, which can benefit from accurate decision support
methodologies that inherently generate trust. In this work, we propose FPT,
(MedFP), a novel method that combines probabilistic trees and fuzzy logic to
assist clinical practice. This approach is fully interpretable as it allows
clinicians to generate, control and verify the entire diagnosis procedure; one
of the methodology's strength is the capability to decrease the frequency of
misdiagnoses by providing an estimate of uncertainties and counterfactuals. Our
approach is applied as a proof-of-concept to two real medical scenarios:
classifying malignant thyroid nodules and predicting the risk of progression in
chronic kidney disease patients. Our results show that probabilistic fuzzy
decision trees can provide interpretable support to clinicians, furthermore,
introducing fuzzy variables into the probabilistic model brings significant
nuances that are lost when using the crisp thresholds set by traditional
probabilistic decision trees. We show that FPT and its predictions can assist
clinical practice in an intuitive manner, with the use of a user-friendly
interface specifically designed for this purpose. Moreover, we discuss the
interpretability of the FPT model.

本文提出了一种基于概率树和模糊逻辑的全面可解释的诊断辅助方法 FPT (MedFP)，并将其作为一个概念验证应用于甲状腺结节分型和慢性肾脏疾病进展风险预测中，在提供较少错诊的同时提供意外事件及诊断不确定性的判断。实验结果表明，使用模糊变量的概率模型可以提供更多细节，并且 FPT 和其预测在医疗实践中具有直观的辅助作用，并配备专门为此目的设计的用户界面。