Accurately predicting patient expenditure in healthcare is an important task with many applications such as provider profiling, accountable care management, and capitated medical payment adjustment. Existing approaches mainly rely on manually designed features and linear regression-based models, which require massive medical domain knowledge and show limited predictive performance. This paper proposes a multi-view deep learning framework to predict future healthcare expenditure at the individual level based on historical claims data. Our multi-view approach can effectively model the heterogeneous information, including patient demographic features, medical codes, drug usages, and facility utilization. We conducted expenditure forecasting tasks on a real-world pediatric dataset that contains more than 450,000 patients. The empirical results show that our proposed method outperforms all baselines for predicting medical expenditure. These findings help toward better preventive care and accountable care in the healthcare domain.

中文翻译：

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预测医疗保健中患者支出的多视图深度学习框架

准确预测患者在医疗保健方面的支出是许多应用程序中的重要任务，例如提供者配置文件，负责任的护理管理和有针对性的医疗费用调整。现有方法主要依靠手动设计的功能和基于线性回归的模型，这些模型需要大量的医学领域知识并且显示出有限的预测性能。本文提出了一种多视图深度学习框架，可以基于历史索赔数据预测个人层面的未来医疗保健支出。我们的多视图方法可以有效地对异类信息进行建模，包括患者的人口统计特征，医疗法规，药物使用和设施使用情况。我们对包含超过450,000名患者的真实儿科数据集进行了支出预测任务。实证结果表明，我们提出的方法在预测医疗费用方面优于所有基线。这些发现有助于在医疗保健领域提供更好的预防性护理和负责任的护理。