Automated decision making via optimization and machine learning

通过优化和机器学习自动决策

• 批准号: RGPIN-2020-04082
• 负责人: Chan, Timothy
• 金额: $ 3.79万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716794
• 关键词: Automated; decision; making; via; optimization

This research program will develop a new automated decision making platform at the intersection of inverse optimization, robust optimization and machine learning. Many decision making problems combine optimization and machine learning. For example, an optimization model that generates decisions may use auxiliary parameters predicted via machine learning. To train the machine learning model, one needs data on the parameters. However, it is often easier and more relevant to obtain data on the desired solutions directly. Thus, the goal of my Discovery program is to develop new computational tools for this predict-then-reconstruct paradigm. In particular, I will use machine learning to predict desirable characteristics of an optimal solution. Then, I will develop new inverse optimization methods to learn parameters of a robust optimization model that can generate a solution with the desired properties. The framework will be automated in the sense all that one needs are basic covariates that relate to solution quality; with this data, machine learning, then inverse optimization, then robust optimization can be implemented without human intervention. The main application in this research program is radiation therapy treatment planning. Radiation therapy is one of the primary ways to treat cancer. The current process to design a treatment is inefficient, relying on manual trial-and-error effort. The challenge stems from the difficulty in determining appropriate parameters in the planning software that will produce an acceptable treatment. Using my predict-then-reconstruct approach, I will demonstrate automated treatment plan generation. A deep learning model trained on historical treatments will predict a clinically acceptable dose distribution for a patient, given only imaging data. Then, an inverse optimization model will learn parameters of a robust optimization model that will create a deliverable treatment plan with clinically desirable characteristics. My automated planning approach will simultaneously improve efficiency and process standardization without sacrificing treatment personalization. While the example application is radiation therapy, the general methodology will be broadly applicable. Optimization and machine learning are fast-growing fields that are in short supply of graduates. My trainees will develop highly employable skills that will be valued in data-driven industries like healthcare, transportation, manufacturing, finance, supply chain management, energy and defense. The application of my research to radiation therapy also has major economic potential. Radiation therapy is used to treat half of all cancer patients, but there is a looming shortage of skilled personnel to design and deliver treatments. My research helps close the demand-supply gap and allows radiation therapy to be delivered at scale, particularly important in developing countries. Thus, my research represents a made-in-Canada innovation with global impact.

该研究计划将在逆优化，鲁棒优化和机器学习的交叉点上开发一个新的自动决策平台。许多决策问题结合了联合收割机优化和机器学习。例如，生成决策的优化模型可以使用经由机器学习预测的辅助参数。为了训练机器学习模型，需要参数数据。然而，直接获得关于所需解决方案的数据往往更容易，也更相关。因此，我的探索计划的目标是为这种预测-然后-重建范式开发新的计算工具。特别是，我将使用机器学习来预测最佳解决方案的理想特征。然后，我将开发新的逆优化方法来学习鲁棒优化模型的参数，该模型可以生成具有所需属性的解决方案。该框架将自动化，因为所有需要的都是与解决方案质量相关的基本协变量;有了这些数据，机器学习，然后逆优化，然后鲁棒优化可以在没有人为干预的情况下实现。 在这项研究计划中的主要应用是放射治疗计划。放射治疗是治疗癌症的主要方法之一。目前设计治疗方法的过程效率低下，依赖于人工试错。挑战来自于难以确定规划软件中的适当参数，以产生可接受的治疗。使用我的预测-然后-重建方法，我将演示自动化治疗计划生成。在历史治疗上训练的深度学习模型将预测患者的临床可接受剂量分布，仅给出成像数据。然后，逆优化模型将学习鲁棒优化模型的参数，该鲁棒优化模型将创建具有临床期望特性的可递送治疗计划。我的自动化计划方法将同时提高效率和流程标准化，而不会牺牲治疗个性化。虽然示例应用是放射治疗，但是一般方法将是广泛适用的。 优化和机器学习是快速增长的领域，毕业生供不应求。我的学员将发展高度就业技能，这些技能将在医疗保健，运输，制造业，金融，供应链管理，能源和国防等数据驱动的行业中得到重视。我的研究在放射治疗中的应用也具有巨大的经济潜力。放射疗法用于治疗一半的癌症患者，但设计和提供治疗的熟练人员短缺。我的研究有助于缩小供需缺口，并使放射治疗得以大规模实施，这在发展中国家尤为重要。因此，我的研究代表了具有全球影响力的加拿大制造创新。