Prediction of the healthcare resource utilization using multi-output regression models

Abstract

With the rapidly increasing healthcare cost and the scarcity of inpatient resources, it is of paramount importance to accurately predict the healthcare resource utilization. Previous research mainly focuses on predicting the healthcare cost using single-output models. However, the intensity of the healthcare resource utilization is reflected by multiple measures. For example, the Diagnosis Related Group (DRG) system adopted in China measures the healthcare resource utilization using both cost and length of stay (LoS), which motivates us to jointly predict these two measures. Compared to constructing several independent single-output models for each task, using multi-output models can provide unified prediction rules, reduce the training time, and improve the generalization by leveraging the correlations across tasks. We utilize four multi-output machine learning models, including the multi-task Lasso, the decision tree, the random forest, and the neural network. We evaluate their performance based on the Electronic Health Record (EHR) dataset with approximately 750,000 records. Based on extensive numerical experiments, we provide a guideline for model selection and construction. This research has the potential to improve the management of healthcare resources and provide decision support for healthcare payment system.

Keywords:

• Multi-output regression
• HER
• healthcare management
• machine learning

Additional information

Funding

This research is supported by the National Natural Science Foundation of China under Grants 71210002, 71332005, 71501109 and 71432004.