Polypharmacy, most often defined as the simultaneous consumption of five or more drugs at once, is a prevalent phenomenon in the older population. Some of these polypharmacies, deemed inappropriate, may be associated with adverse health outcomes such as death or hospitalization. Considering the combinatorial nature of the problem as well as the size of claims database and the cost to compute an exact association measure for a given drug combination, it is impossible to investigate every possible combination of drugs. Therefore, we propose to optimize the search for potentially inappropriate polypharmacies (PIPs). To this end, we propose the OptimNeuralTS strategy, based on Neural Thompson Sampling and differential evolution, to efficiently mine claims datasets and build a predictive model of the association between drug combinations and health outcomes. We benchmark our method using two datasets generated by an internally developed simulator of polypharmacy data containing 500 drugs and 100 000 distinct combinations. Empirically, our method can detect up to 72% of PIPs while maintaining an average precision score of 99% using 30 000 time steps.

翻译：多药联用，通常定义为同时服用五种或以上药物，在老年人群中普遍存在。其中部分被视为不适当的多药联用可能与死亡或住院等不良健康结局相关。考虑到该问题的组合特性、索赔数据库的规模以及计算特定药物组合精确关联度量的成本，逐一调查所有可能的药物组合是不可行的。因此，我们提出优化搜索潜在不适当多药联用（PIPs）的方法。为此，我们提出基于神经汤普森采样和差分进化的OptimNeuralTS策略，以高效挖掘索赔数据集并建立药物组合与健康结局之间关联的预测模型。我们使用内部开发的多药联用数据模拟器生成的两个数据集（包含500种药物和10万种独立组合）对方法进行基准测试。实验表明，在30,000个时间步内，该方法可检测高达72%的PIPs，同时维持99%的平均精确率。