Risk adjustment has become an increasingly important tool in healthcare. It has been extensively applied to payment adjustment for health plans to reflect the expected cost of providing coverage for members. Risk adjustment models are typically estimated using linear regression, which does not fully exploit the information in claims data. Moreover, the development of such linear regression models requires substantial domain expert knowledge and computational effort for data preprocessing. In this paper, we propose a novel approach for risk adjustment that uses semantic embeddings to represent patient medical histories. Embeddings efficiently represent medical concepts learned from diagnostic, procedure, and prescription codes in patients' medical histories. This approach substantially reduces the need for feature engineering. Our results show that models using embeddings had better performance than a commercial risk adjustment model on the task of prospective risk score prediction.
翻译:风险调整已成为保健方面日益重要的工具,已广泛应用于保健计划的付款调整,以反映为成员提供保险的预期费用; 风险调整模型通常使用线性回归法估算,而线性回归法没有充分利用索赔数据中的信息; 此外,这种线性回归法模型的开发需要大量的领域专家知识和数据预处理的计算努力; 在本文件中,我们提出了一个新的风险调整办法,利用语义嵌入法来代表病人的医疗史; 嵌入法有效地代表了从诊断、程序和处方代码中汲取的医疗概念; 这种方法大大减少了对特征工程的需要; 我们的结果表明,使用嵌入法模型的性能优于未来风险计数预测任务的商业风险调整模型。