arXiv cs.AI· Jayakody Kankanamalage Chamani Shiranthika·· 10 小时前AI 评分31
基于强化学习的癌症化疗治疗方案控制研究
Reinforcement Learning based control of Cancer Chemotherapy treatments
AI 导读
一项硕士论文研究用离线强化学习从真实人类结肠癌临床数据中生成化疗序贯治疗的最优策略，方法结合保守 Q-learning 与基于模型的离线策略优化算法。研究还通过传统 Q-learning 搭建了化疗仿真，实验评估显示该 Chemo RL 模型能较好地处理化疗给药排程问题，可辅助初级肿瘤科医生做治疗决策。该论文获 2021 年 IEEE 台北分会最佳硕士论文奖。
正文 · 原文
Abstract:Reinforcement Learning has achieved tremendous success in recent years. Extending its scope from the traditional complex games such as Atari, Go, Pokeman, and Chess, reinforcement learning has caught researchers' attention on modelling real-life scenarios. Personalized medicine and the dynamic treatment regimen are two such preliminary research fields; researchers are focusing their mindsets currently. Estimating the optimal dynamic treatment regimens from the observational real clinical data is being popularized among the researchers, widening its scope towards more advanced versions of reinforcement learning algorithms.
This research is about a research study that focused on reinforcement learning techniques that have been approached in the context of cancer chemotherapy treatments. Primarily this thesis addresses the result of the study by means of the state-of-the-art on reinforcement learning techniques used in generating the optimal policy for sequential treatments, including the existing online reinforcement learning approaches. Thereby a simulation for cancer chemotherapy via traditional Q-learning would be presented. Secondly, the study will extend towards using offline reinforcement learning in the optimal policy generation. Thus, the proposed solution follows a data-driven approach, which works on the real human colon cancer clinical data.
The novelty of the proposed solution resides in the implementation of an offline reinforcement learning approach by means of a model-based architecture using the conservative Q-learning and the model-based offline policy optimization algorithm. As per the experimental results and the evaluations, it was revealed that the proposed Chemo RL model caters well the problem of cancer chemotherapy treatment scheduling and serves as a supportive model for the junior oncologists in deciding their treatment decisions.
| Comments: | Masters thesis at the National Taipei University. Received the IEEE Taipei Section, Best Masters thesis award in 2021 |
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2611.12940 [cs.AI] |
| (or arXiv:2611.12940v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.12940 arXiv-issued DOI via DataCite |
Submission history
From: Chamani Shiranthika Jayakody Kankanamalage [view email]
[v1]
Fri, 6 Nov 2026 16:31:55 UTC (7,729 KB)
来源：arXiv cs.AI · arxiv.org