Machine learning methodology for sequential decision support from large-scale longitudinal data

我们提出的研究将发展计算机科学和统计方法，通过提供顺序决策支持，将大量丰富的数据集转化为人类可操作的知识。它建立在我们之前的工作基础上，之前的工作开发了相关的方法，专注于较小的、高度整理的数据集。使用大型数据源面临两个主要挑战：及时处理大型数据集，以及在数据丰富和复杂时有效地将结果传达给最终用户。我们提出的研究将依次解决这两个问题。我们的策略是开发新的机器学习模型，根据数据集中项目的固有属性（“特征”）和数据集中项目的相似或不同（通过“内核”）做出决策。这种方法将通过凸优化导致有效的数据处理，并将通过可视化技术导致可解释的结果。这些方法结合起来将能够使用以前未开发的大型数据集来提供顺序决策支持。当前做出好的决策通常需要了解未来可能做出的决策——如果一个决策是基于对未来决策可能性的了解做出的，我们就说这个决策是“非短视的”。非近视决策在许多应用领域都很重要；例如，我们现在看到电子数据源的发展，记录了数千甚至数百万患者随着时间的推移对不同治疗顺序的反应，这些有可能比以前的研究更有效地为循证非近视医疗决策提供信息。然而，从这些数据中提取证据并有效地将其呈现给医生的严格分析技术（例如通过个性化治疗建议）仍处于起步阶段。强化学习和机器学习中的计算机科学方法具有巨大的潜力，但在许多方面并不适合提供这种情况下所需的证据。我们的长期研究目标是开发强化学习和机器学习技术，以便它们可以应用于这些新的连续医疗数据来源，并反过来为医生提供非近视决策的最佳可用证据。随着现有连续医疗数据的深度和广度的增加，我将开发的方法将为我们的医生提供新的、高质量的证据，帮助他们为病人选择最好的治疗方法，从而改善加拿大的医疗保健服务。

Our proposed research will develop computer science and statistical methods for turning large, rich data sets into human-actionable knowledge by providing sequential decision support. It builds on our previous work, which developed related methods that focused on smaller, highly-curated datasets. Using large data sources presents two major challenges: processing large datasets in a timely manner, and effectively communicating the results to an end-user when data are rich and complicated. Our proposed research will tackle both of these issues in turn. Our strategy is to develop new machine learning models that make decisions based both on inherent properties of the items in the data set ("features") and on how items in the dataset are similar or different (through "kernels".) This approach will lead to efficient data processing through convex optimization, and will lead to interpretable results presented through visualization techniques. These methods combined will be able to use large, previously un-tapped data sets to provide sequential decision support.Making good decisions in the present often requires knowledge of potential decisions to be made in the future -- we say a decision is "non-myopic" if it is made based on knowledge of the potential for future decision-making. Non-myopic decision making is important in many application areas; for example, are now seeing the development of electronic data sources that record how thousands or even millions of patients respond to different sequences of treatments over time, and these have the potential to inform evidence-based non-myopic medical decision making more effectively than previous studies. However, rigorous analysis techniques for extracting evidence from this data and effectively presenting it to physicians -- for example through personalized treatment recommendations -- are still in their infancy. Computer science methods in reinforcement learning and machine learning have enormous potential, but in many ways are not suited to providing the evidence required in this setting. Our long-term research goal is to develop reinforcement learning and machine learning techniques so they can be applied to these new sources of sequential medical data, and can in turn provide doctors with the best available evidence for non-myopic decision making. As the depth and breadth of available sequential medical data increases, the methods I will develop will improve the delivery of health care in Canada by providing our medical doctors with new, high-quality evidence to aid them in choosing the best treatments for their patients.