In healthcare applications, understanding how machine/deep learning models make decisions is crucial. In this study, we introduce a neural network framework, $\textit{Truth Table rules}$ (TT-rules), that combines the global and exact interpretability properties of rule-based models with the high performance of deep neural networks. TT-rules is built upon $\textit{Truth Table nets}$ (TTnet), a family of deep neural networks initially developed for formal verification. By extracting the necessary and sufficient rules $\mathcal{R}$ from the trained TTnet model (global interpretability) to yield the same output as the TTnet (exact interpretability), TT-rules effectively transforms the neural network into a rule-based model. This rule-based model supports binary classification, multi-label classification, and regression tasks for small to large tabular datasets. After outlining the framework, we evaluate TT-rules' performance on healthcare applications and compare it to state-of-the-art rule-based methods. Our results demonstrate that TT-rules achieves equal or higher performance compared to other interpretable methods. Notably, TT-rules presents the first accurate rule-based model capable of fitting large tabular datasets, including two real-life DNA datasets with over 20K features.

翻译：在医疗应用中，理解机器学习/深度学习模型的决策过程至关重要。本研究提出一种神经网络框架——真理表规则（TT-rules），该框架融合了基于规则的模型的全局精确可解释性与深度神经网络的高性能特性。TT-rules 基于真理表网络（TTnet）构建，后者是一类最初为形式化验证而开发的深度神经网络家族。通过从训练好的TTnet模型中提取必要且充分的规则集 $\mathcal{R}$（全局可解释性），使其输出与TTnet完全一致（精确可解释性），TT-rules 有效将神经网络转化为基于规则的模型。该规则模型支持对中小型至大型表格数据集执行二分类、多标签分类及回归任务。在阐述框架原理后，我们评估了TT-rules在医疗应用中的性能，并与最先进的基于规则的方法进行对比。结果表明，TT-rules 在性能上可与其它可解释方法持平甚至更优。值得注意的是，TT-rules 是首个能够拟合大型表格数据集（包括两个特征数超过2万的真实DNA数据集）的精准规则模型。