From enrichment to insights

项目摘要 大多数医疗决定是在没有严格证据支持的情况下做出的，这在很大程度上是由于成本问题。 以及在大多数临床情况下进行随机试验的复杂性。在实践中，临床医生必须 根据自己和同事的集体经验作出判断。的 电子健康记录（EHR）的出现使现代医生能够通过算法检查 成千上万或数百万患者的记录，以快速找到类似的情况下，并比较结果。 除了填补可诉证据中的推理空白外，这类分析还避免了 伦理、实用性和普遍性困扰着随机临床试验（RCT）。不幸的是， 识别具有适当表型的患者，适当利用可用数据调整 结果和匹配相似的患者以减少混淆仍然是每项研究的关键挑战 使用EHR数据。克服这些挑战以提高观察性研究的准确性 使用EHR数据进行分析至关重要。 使用EHR数据的研究开始，首先定义一组具有特定表型的患者，类似于 为一项临床试验聚集一批人这种电子表型分析的过程通常是通过一套 由专家定义的规则。机器学习方法越来越多地用于补充 我们提出了几个进步来验证和改进这一点 实践我们将探索和量化特征工程选择的影响，以改变 诊断，程序，药物，实验室测试和临床记录在EHR到一个可计算的 特征矩阵最后，基于最近的进展，我们计划描述 现有的方法，并制定EHR的具体战略，病人匹配。 我们的工作意义重大，因为我们将承担三个具有挑战性的问题-电子表型， 特征工程和患者匹配--这些都是通过EHR数据产生洞察力的障碍。如果 如果我们成功了，我们将大大提高我们从大量信息中产生见解的能力， 作为临床过程的副产品，

Project Summary Most medical decisions are made without the support of rigorous evidence in large part due to the cost and complexity of performing randomized trials for most clinical situations. In practice, clinicians must use their judgement, informed by their own and the collective experience of their colleagues. The advent of the electronic health record (EHR) enables the modern practitioner to algorithmically check the records of thousands or millions of patients to rapidly find similar cases and compare outcomes. In addition to filling the inferential gap in actionable evidence, these kinds of analyses avoid issues of ethics, practicality, and generalizability that plague randomized clinical trials (RCTs). Unfortunately, identifying patients with the appropriate phenotypes, properly leveraging available data to adjust results, and matching similar patients to reduce confounding remain critical challenges in every study that uses EHR data. Overcoming these challenges to improve the accuracy of observational studies conducted with EHR data is of paramount importance. Studies using EHR data begin by defining a set of patients with specific phenotypes, analogous to amassing a cohort for a clinical trial. This process of electronic phenotyping, is typically done via a set of rules defined by experts. Machine learning approaches are increasingly used to complement consensus definitions created by experts and we propose several advances to validate and improve this practice. We will explore and quantify the effects of feature engineering choices to transform the diagnoses, procedures, medications, laboratory tests and clinical notes in the EHR into a computable feature matrix. Finally, building on recent advances, we plan to characterize the performance of existing methods and develop EHR-specific strategies for patient matching. Our work is significant because we will take on three challenging problems--electronic phenotyping, feature engineering, and patient matching--that stand in the way of generating insights via EHR data. If we are successful, we will significantly advance our ability to generate insights from the large amounts of health data that are routinely generated as a byproduct of clinical processes.