高模型不确定性下癌症混合治疗药物释放的基于学习的灵敏度分析与反馈设计
Learning-Based sensitivity analysis and feedback design for drug delivery of mixed therapy of cancer in the presence of high model uncertainties
摘要：
在本文中，提出了一种方法，能够分析的灵敏度的治疗结果不可避免的高分散的患者的具体参数，一方面和选择的参数，定义的药物输送反馈策略的另一方面。更确切地说，给出了一种方法，该方法能够提取和排名最有影响力的参数，这些参数确定了在不确定性的实现云上针对给定的一组初始条件的给定反馈治疗的成功/失败的概率。此外，还可以推导出药物使用量预期的预测指标。这使得能够设计有效的随机优化框架，该框架保证肿瘤的安全收缩，同时最小化所使用的不同药物的量的加权和。该框架的说明和验证使用的混合治疗癌症的例子，涉及三种组合药物，即：化疗药物，免疫疫苗和免疫治疗药物。最后，在这个特定的情况下，它表明，仪表板可以建立在2D空间的最有影响力的状态组件，总结结果的概率和相关的药物使用的等值曲线在减少状态空间。
英文摘要：
In this paper, a methodology is proposed that enables to analyze the sensitivity of the outcome of a therapy to unavoidable high dispersion of the patient specific parameters on one hand and to the choice of the parameters that define the drug delivery feedback strategy on the other hand. More precisely, a method is given that enables to extract and rank the most influent parameters that determine the probability of success/failure of a given feedback therapy for a given set of initial conditions over a cloud of realizations of uncertainties. Moreover predictors of the expectations of the amounts of drugs being used can also be derived. This enables to design an efficient stochastic optimization framework that guarantees safe contraction of the tumor while minimizing a weighted sum of the quantities of the different drugs being used. The framework is illustrated and validated using the example of a mixed therapy of cancer involving three combined drugs namely: a chemotherapy drug, an immunology vaccine and an immunotherapy drug. Finally, in this specific case, it is shown that dash-boards can be built in the 2D-space of the most influent state components that summarize the outcomes' probabilities and the associated drug usage as iso-values curves in the reduced state space.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
