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

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  • Hongzhen Tian
  • Reuven Zev Cohen
  • Chuck Zhang
  • Yajun Mei

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.

Suggested Citation

  • Hongzhen Tian & Reuven Zev Cohen & Chuck Zhang & Yajun Mei, 2023. "Active learning-based multistage sequential decision-making model with application on common bile duct stone evaluation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 50(14), pages 2951-2969, October.
    • Handle: RePEc:taf:japsta:v:50:y:2023:i:14:p:2951-2969
    DOI: 10.1080/02664763.2023.2164885
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