CAREER: Algorithms and Decision Models for Learning in Health Care Systems

职业：医疗保健系统中学习的算法和决策模型

• 批准号: 1554140
• 负责人: Mohsen Bayati
• 金额: $ 50万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1554140&HistoricalAwards=false
• 关键词: CAREER; Algorithms; Decision; Models; Learning

This Faculty Early Career Development (CAREER) grant aims to build new algorithms and mathematical models for optimizing decisions in healthcare systems. Growing availability of digital health records has created many opportunities for transforming health care delivery facilities such as hospitals, potentially improving their quality and operational efficiency. For example, recent research shows the benefits of guiding decisions in a hospital via statistical predictions. However, predictions are worthless when they cannot be integrated with the organization's workflow. In this regard, new ways of combining predictions with operations research models are needed. This research tackles mathematical barriers to the integration of operations research and statistical modeling. An example of this integration would be building new optimization algorithms that can adapt to patterns of uncertainty in the data at hand. The findings of this research can potentially improve quality of medical care and reduce health care costs. In addition, because this research lies at the intersection of machine learning, medicine, operations research, and statistics, it can help train the next generation of academic scholars (with emphasis on underrepresented groups) on this multidisciplinary area.A common theme of statistical models in the age of big data is to characterize uncertainty via parametric or non-parametric models in a "high dimensional" space. High dimensional space refers to the space of available predictor variables that could contain information about uncertain outcomes in a decision task. Models based on high-dimensional data are particularly useful in healthcare because healthcare systems are complex, with many system-specific features in their patient populations and practice patterns. Such potential applications have led to a large body of recent literature (known by high-dimensional statistics) to deal with algorithmic and statistical challenges of such modeling framework. In contrast, in the operations research literature, the uncertainty is typically restricted to few known probability distributions to make mathematical analysis more tractable. This research aims to relax the aforementioned restrictions by combining ideas from the high-dimensional statistics with the mathematics of operations research, and applying the resulting models to two application areas -- wait time prediction in emergency departments, and personalized administration of new treatments. For example, in settings that can be modeled as multi-armed bandit problems, new theory that can help reduce the uncertainty in decisions by utilizing availability of many predictor variables in addition to the data on past decisions can be useful. Reducing this knowledge gap requires developing a new class of algorithms and asymptotic theory when the number of predictors grows faster than time periods or the number of samples. Another challenge that needs to be addressed is that in statistical setting the samples are usually assumed to be independent, however this assumption fails in decision systems with feedback where current decisions may impact future samples.

该教师早期职业发展（CAREER）赠款旨在建立新的算法和数学模型，以优化医疗保健系统的决策。数字健康记录的日益普及为医院等医疗保健服务设施的转型创造了许多机会，有可能提高其质量和运营效率。例如，最近的研究表明通过统计预测指导医院决策的好处。然而，当预测无法与组织的工作流程集成时，预测就毫无价值。在这方面，需要将预测与运筹学模型相结合的新方法。这项研究解决了运筹学和统计建模整合的数学障碍。这种集成的一个例子是构建新的优化算法，该算法可以适应手头数据中的不确定性模式。这项研究的结果可能会提高医疗保健质量并降低医疗保健成本。此外，由于这项研究位于机器学习、医学、运筹学和统计学的交叉点，它可以帮助在这个多学科领域培养下一代学术学者（重点是代表性不足的群体）。大数据时代统计模型的一个共同主题是通过“高维”空间中的参数或非参数模型来表征不确定性。高维空间是指可用预测变量的空间，其中可能包含有关决策任务中不确定结果的信息。基于高维数据的模型在医疗保健中特别有用，因为医疗保健系统很复杂，其患者群体和实践模式具有许多系统特定的功能。这种潜在的应用催生了大量近期文献（以高维统计数据为基础）来应对此类建模框架的算法和统计挑战。相比之下，在运筹学文献中，不确定性通常仅限于少数已知的概率分布，以使数学分析更容易处理。本研究旨在通过将高维统计的思想与运筹学的数学相结合，并将所得模型应用于两个应用领域——急诊科的等待时间预测和新治疗的个性化管理，从而放宽上述限制。例如，在可以建模为多臂赌博机问题的环境中，除了过去决策的数据之外，还可以利用许多预测变量的可用性来帮助减少决策的不确定性的新理论可能会很有用。当预测变量数量的增长速度快于时间段或样本数量的增长速度时，缩小这种知识差距需要开发一类新的算法和渐近理论。需要解决的另一个挑战是，在统计设置中，样本通常被假设为独立的，但是这种假设在带有反馈的决策系统中失败，因为当前的决策可能会影响未来的样本。