English(EN) Adaptive Chemotherapy Control under Tumor Heterogeneity via Reinforcement Learning

强化学习模型在适应性化疗控制方面展现出潜力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-14 04:00

研究人员开发并比较了用于适应性化疗控制的闭环深度强化学习（DRL）策略，同时利用了连续（TD3）和离散（DQN）动作空间。这些DRL策略在一个复杂异质性肿瘤模型上进行了训练，并与基于Pontryagin最大值原理（PMP）推导出的传统开环方法进行了基准测试。该研究评估了这些策略在100名具有扰动生长和药物敏感性参数的虚拟患者队列中的泛化能力，揭示了肿瘤缩小与剂量一致性之间的权衡。 AI

影响展示了在复杂医疗场景中，AI驱动的个性化治疗计划的潜力。

排序理由在arXiv上发表的研究论文，详细介绍了强化学习的新应用。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Bereket Sitotaw Kidane, Md Samiul Haque Motayed, Shuo Wang · 2026-09-14 04:00

基于强化学习的肿瘤异质性适应性化疗控制

arXiv:2609.12264v1 Announce Type: new Abstract: Designing effective chemotherapy regimens is hindered by tumor heterogeneity and drug resistance, which complicate the deployment of patient-specific model-based optimal control across diverse populations. We develop and compare clo…

本文如何被排名

报道来源 [1]

基于强化学习的肿瘤异质性适应性化疗控制

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