From enrichment to insights

项目摘要 大多数医疗决定都是在没有严格证据的情况下做出的，这在很大程度上是因为成本 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 使用他们的判断力，根据他们自己和他们同事的集体经验。这个 电子健康记录(EHR)的出现使现代从业者能够通过算法检查 数以千计或数以百万计的患者的记录可以快速找到类似的病例并比较结果。 除了填补可诉讼证据的推理空白之外，这些类型的分析还避免了以下问题 困扰随机临床试验(RCT)的伦理、实用性和普适性。不幸的是， 识别具有适当表型的患者，适当利用可用数据进行调整 结果，匹配相似的患者以减少混淆仍然是每项研究的关键挑战 使用电子病历数据的公司。克服这些挑战，提高观测研究的准确性 使用电子病历数据进行调查是至关重要的。 使用EHR数据的研究首先定义了一组具有特定表型的患者，类似于 为一项临床试验聚集了一群人。这一电子表型的过程通常通过一组 由专家定义的规则。机器学习方法越来越多地被用来补充 由专家创建的共识定义，我们提出了几个改进措施来验证和改进这一点 练习一下。我们将探索和量化功能工程选择的影响，以转变 将电子病历中的诊断、程序、药物、实验室测试和临床记录输入到计算机中 特征矩阵。最后，在最新进展的基础上，我们计划描述 现有的方法，并开发特定于EHR的患者匹配策略。 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， 功能工程和患者匹配--这些都阻碍了通过电子病历数据产生洞察力。如果 我们是成功的，我们将显著提高我们从大量数据中产生洞察力的能力 健康数据通常是作为临床过程的副产品生成的。

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.