Personalized Treatment Policies with the Novel Buckley-James Q-Learning Algorithm

被引：3

作者：

Lee, Jeongjin ^{[1
]}

Kim, Jong-Min ^{[2
]}

机构：

[1] Ohio State Univ, Dept Stat, Columbus, OH 43210 USA

[2] Univ Minnesota Morris, Stat Discipline, Morris, MN 56267 USA

来源：

AXIOMS | 2024年 / 13卷 / 04期

关键词：

Q-learning; reinforcement learning; precision medicine; Buckley-James Method; survival analysis; DYNAMIC TREATMENT REGIMES; LINEAR-REGRESSION; SURVIVAL;

D O I：

10.3390/axioms13040212

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

This research paper presents the Buckley-James Q-learning (BJ-Q) algorithm, a cutting-edge method designed to optimize personalized treatment strategies, especially in the presence of right censoring. We critically assess the algorithm's effectiveness in improving patient outcomes and its resilience across various scenarios. Central to our approach is the innovative use of the survival time to impute the reward in Q-learning, employing the Buckley-James method for enhanced accuracy and reliability. Our findings highlight the significant potential of personalized treatment regimens and introduce the BJ-Q learning algorithm as a viable and promising approach. This work marks a substantial advancement in our comprehension of treatment dynamics and offers valuable insights for augmenting patient care in the ever-evolving clinical landscape.

引用

页数：12

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