Active learning-based multistage sequential decision-making model with application on common bile duct stone evaluation

Abstract

Multistage sequential decision-making occurs in many real-world applications such as healthcare diagnosis and treatment. One concrete example is when the doctors need to decide to collect which kind of information from subjects so as to make the good medical decision cost-effectively. In this paper, an active learning-based method is developed to model the doctors' decision-making process that actively collects necessary information from each subject in a sequential manner. The effectiveness of the proposed model, especially its two-stage version, is validated on both simulation studies and a case study of common bile duct stone evaluation for pediatric patients.

Keywords:

• Active learning
• incomplete data
• ordinal logistic model
• sequential decision

Acknowledgements

The authors are grateful to the journal editor, Dr. Jie Chen, the associate editor, and anonymous reviewers for their constructive comments that significantly improve the quality and presentation of this article. This research was supported in part by NSF [grant number DMS-2015405].

Disclosure statement

No potential conflict of interest was reported by the author(s).