Patient expenditure prediction using deep learning framework

Abstract

Measurement of patient expenditure in healthcare is a critical task that has a variety of applications, including provider profiling, accountable care management, and capitated medical payment adjustment. Currently available methods rely on manually built features and linear regression-based models, both of which need a significant amount of medical domain expertise. and have low prediction accuracy. This study develops a multi-view deep learning system for forecasting future healthcare costs at the individual level based on prior claims data. Our multi-view technique efficiently models heterogeneous data such patient demographics, medical codes, medication usages, and facility usage. To execute spending forecasting tasks, we employed a real-world paediatric dataset with approximately 450,000 patients. According to the empirical data, our proposed technique beats all baselines for predicting medical cost. In the sphere of healthcare, these insights help to improve preventative and responsible care.