INTERPRETABLE CLASSIFIERS USING RULES AND BAYESIAN ANALYSIS: BUILDING A BETTER STROKE PREDICTION MODEL
INTERPRETABLE CLASSIFIERS USING RULES AND BAYESIAN ANALYSIS: BUILDING A BETTER STROKE PREDICTION MODEL
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DOI：
10.1214/15-aoas848
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发表时间：
2015-09-01
影响因子：
1.8
通讯作者：
Madigan, David
中科院分区：
文献类型：
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作者：
Letham, Benjamin;Rudin, Cynthia;Madigan, David
我们的目标是生成不仅准确，而且可由人类专家解释的预测模型。我们的模型是决策列表，其中包括一系列的如果...然后...语句（例如，如果是高血压，则是中风），其将高维、多变量特征空间离散化为一系列简单、易于解释的决策语句。我们引入了一个生成模型称为贝叶斯规则列表，产生一个后验分布可能的决策列表。它采用了一种新的先验结构，以鼓励稀疏。我们的实验表明，贝叶斯规则列表具有与当前机器学习中的顶级预测算法相当的预测准确性。我们的方法是由个性化医疗的最新发展，并可用于产生高度准确和可解释的医疗评分系统的动机。我们通过产生CHADS（2）评分的替代方案来证明这一点，CHADS（2）评分在临床实践中被积极用于估计房颤患者的卒中风险。我们的模型与CHADS（2）一样可解释，但更准确。
We aim to produce predictive models that are not only accurate, but are also interpretable to human experts. Our models are decision lists, which consist of a series of if... then... statements (e.g., if high blood pressure, then stroke) that discretize a high-dimensional, multivariate feature space into a series of simple, readily interpretable decision statements. We introduce a generative model called Bayesian Rule Lists that yields a posterior distribution over possible decision lists. It employs a novel prior structure to encourage sparsity. Our experiments show that Bayesian Rule Lists has predictive accuracy on par with the current top algorithms for prediction in machine learning. Our method is motivated by recent developments in personalized medicine, and can be used to produce highly accurate and interpretable medical scoring systems. We demonstrate this by producing an alternative to the CHADS(2) score, actively used in clinical practice for estimating the risk of stroke in patients that have atrial fibrillation. Our model is as interpretable as CHADS(2), but more accurate.