A Multi-way Multi-task Learning Approach for Multinomial Logistic Regression An Application in Joint Prediction of Appointment Miss-opportunities across Multiple Clinics

摘要

Objectives: Whether they have been engineered for it or not, most healthcare systems experience a variety of unexpected events such as appointment miss-opportunities that can have significant impact on their revenue, cost and resource utilization. In this paper, a multi-way multi-task learning model based on multinomial logistic regression is proposed to jointly predict the occurrence of different types of miss-opportunities at multiple clinics.