Explainable and Personalized Medical Cost Prediction Based on Multitask Learning over Mobile Devices

Abstract

Currently, the forecasting of healthcare costs is of significant importance for the finance management of both government and individual citizens. However, the existence of dramatic individual diversity in health status, as well as the extensive complexity of the factors influencing the cost, has made the prediction a challenging task. Thanks to the unprecedented adoption of mobile devices, regular individuals may contribute diverse dimensions of data for the medical cost prediction. Hospitals and healthcare service providers are all setting up their own mobile services and collect user data for analysis. Previous methods usually employed traditional machine learning or simple neural network methods, which are difficult to be applied to the nonlinear medical cost and diverse dimensions of data. Therefore, this paper proposes a multitask learning-based framework for interpretable medical cost interval prediction to address these issues. The framework proposed in this paper first predicts subcost intervals by applying the multidimensional data collected from mobile ends and following the multitask learning paradigm. The total cost interval is then predicted based on this prediction. Simultaneously, the framework derives a decision tree from the parameters of the multitask learning network and calculates the importance of each feature in predicting the cost intervals. This paper demonstrates the method's effectiveness using real-world data experiments.