Objective

To study the feasibility of evaluating feature importance with Shapley Values and ensemble methods in the context of pharmacoepidemiology and medication safety.

Methods

We detected medications associated with Alzheimer's disease (AD) by examining the additive feature attribution with combined approach of Gradient Boosting and Shapley Values in the Medication use and Alzheimer's disease (MEDALZ) study, a nested case-control study of 70,719 verified AD cases in Finland. Our methodological approach is to do binary classification using Gradient boosting (an ensemble of weak classifiers) in a supervised learning manner. Then we apply Shapley Values (from cooperative game theory) to analyze how feature combinations affect the classification result. Medication use with a five to one year time-window before AD diagnosis was ascertained from Prescription register.

Results

Antipsychotics with low or medium dose, antidepressants with medium to high dose, and cardiovascular medications with medium to high dose were identified as the contributing features for separating cases with AD from controls. Medium to high amount of irregularity in the purchase pattern were an indicating feature for separating AD cases from controls. The similarity of medication purchases between AD cases and controls made the feature evaluation challenging.

Conclusions

The combined approach of Gradient Boosting and feature evaluation with Shapley Values identified features that were consistent with findings from previous hypothesis-driven studies. Additionally, the results from the additive feature attribution identified new candidates for future studies on AD risk factors. Our approach also shows promise for studies based on observational studies, where feature identification and interactions in populations are of interest; and the applicability of using Shapley Values for evaluating feature relevance in pattern recognition tasks.

中文翻译：

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使用集成方法和合作博弈理论检测与阿尔茨海默氏病相关的药物。

目的

在药物流行病学和药物安全性的背景下，研究使用Shapley值和集成方法评估特征重要性的可行性。

方法

我们通过结合药物使用和阿尔茨海默氏病（MEDALZ）研究中的梯度增强和Shapley值相结合的方法检查加性特征归因，检测了与阿尔茨海默氏病（AD）相关的药物。我们的方法论方法是以有监督的学习方式使用梯度提升（弱分类器的集合）进行二进制分类。然后，我们运用Shapley值（来自合作博弈论）来分析特征组合如何影响分类结果。从处方处方中确定在进行AD诊断之前需要5到1年的时间使用药物。

结果

低剂量或中等剂量的抗精神病药，中等剂量至高剂量的抗抑郁药以及中等剂量至高剂量的心血管药物被确定为将AD患者与对照组分开的重要特征。购买模式中的中度到大量不规则行为是将AD案件与对照组分开的一个指示性特征。AD病例和对照组之间购买药物的相似性使得特征评估具有挑战性。

结论

渐变增强和特征评估与Shapley值相结合的方法确定了与以前的假设驱动研究结果一致的特征。此外，加性特征归因的结果为未来AD危险因素研究确定了新的候选对象。我们的方法还显示了基于观察性研究的希望，在这些研究中，特征识别和人群交互作用是令人感兴趣的。以及在模型识别任务中使用Shapley值评估特征相关性的适用性。