Statistical Methods for Incorporating Machine Learning Tools in Inference and Large-Scale Surveillance using Electronic Medical Records Data

总结 临床护理信息系统的现代化和标准化正在创建大型的 链接的电子健康记录（EHR），用于捕获关键治疗并选择患者结果， 全国数百万患者。从这些系统中得到的观测数据 提供了一个无与伦比的机会来了解现有的和新的治疗方法的有效性， 并监测在广泛患者中使用干预措施时可能出现的潜在安全性问题 人口数量。然而，观察性临床数据的暴露受许多因素驱动， 因此，需要进行积极的调整，以尽可能多地消除混杂偏倚， 对选定的风险进行归因。机器学习领域提供了一个强大的 收集数据驱动的方法，以进行灵活，彻底的混杂调整，但 当这些技术被用作 分析战略的一部分。我们建议通过开发和改进可重复的研究方法， 说明了利用机器学习方法的灵活性来 使用大规模EHR数据检测和表征健康影响信号。 具体来说，我们将首先开发技术，使有效的，统计上有效的和强大的推理 使用最先进的机器学习工具来评估治疗效果我们还将发展在线学习 技术，使这样的推理在流EHR数据的上下文中。方法上的进步将 使我们能够制定一个正式、严格和切实可行的框架， 并对安全性终点进行可靠的监测。最后，我们将开发统计方法， 结合先前信息-包括人口统计学、流行病学或药效学 知识，例如-以改善健康影响的估计和推断时，健康结果 感兴趣的是罕见的，因此统计问题是困难的，因为经常发生在安全监督。 拟议研究的最终目标是使生物医学研究人员和公共卫生 监管机构仔细监测和保护公众的健康，让他们更有效地 并且更可靠地检测可能包含在人群规模EHR中的关键健康影响信号 数据

SUMMARY The modernization and standardization of clinical care information systems is creating large networks of linked electronic health records (EHR) that capture key treatments and select patient outcomes for millions of patients throughout the country. The observational data emerging from these systems provide an unparalleled opportunity to learn about the effectiveness of existing and novel treatments, and to monitor potential safety issues that may arise when interventions are used in broad patient populations. However, observational clinical data have exposures that are driven by many factors and therefore aggressive adjustment is needed to remove as much confounding bias as possible in order to make attribution regarding select exposures. The field of machine learning provides a powerful collection of data-driven approaches for performing flexible, thorough confounding adjustment, but performing reliable statistical inference is particularly challenging when these techniques are used as part of the analytic strategy. We propose to advance reproducible research methods by developing and illustrating novel targeted learning tools that leverage the flexibility of machine learning methods to detect and characterize health effect signals using large-scale EHR data. Specifically, we will first develop techniques for making efficient, statistically valid and robust inference for treatment effects using state-of-the-art machine learning tools. We will also develop online learning techniques to make such inference in the context of streaming EHR data. Methodological advances will enable us to formulate a formal, rigorous and practical framework for conducting continuous, effective and reliable surveillance for safety endpoints. Finally, we will develop statistical approaches for incorporating prior information -- including demographic, epidemiologic or pharmacodynamic knowledge, for example -- to improve health effect estimation and inference when the health outcome of interest is rare and the statistical problem is thus difficult, as often occurs in safety surveillance. The ultimate goal of the proposed research is to enable biomedical researchers and public health regulators to carefully monitor and protect the health of the public by allowing them to more effectively and more reliably detect critical health effect signals that may be contained in population-scale EHR data.