Active learning-based multistage sequential decision-making model with application on common bile duct stone evaluation
Active learning-based multistage sequential decision-making model with application on common bile duct stone evaluation
复制标题
基于主动学习的多阶段序贯决策模型在胆总管结石评价中的应用
DOI：
10.1080/02664763.2023.2164885
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发表时间：
2022-01
影响因子：
1.5
通讯作者：
Hongzhen Tian;R. Cohen;Chuck Zhang;Yajun Mei
中科院分区：
文献类型：
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作者：
Hongzhen Tian;R. Cohen;Chuck Zhang;Yajun Mei
多阶段顺序决策发生在许多现实世界的应用，如医疗诊断和治疗。一个具体的例子是，当医生需要决定从受试者那里收集哪种信息，以便以具有成本效益的方式做出良好的医疗决策时。在本文中，一个主动学习为基础的方法来模拟医生的决策过程，积极收集必要的信息，从每个主题的顺序方式。该模型的有效性，特别是其两个阶段的版本，验证了模拟研究和案例研究的胆总管结石评价儿科患者。
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.