Bionformatics Resource Core

项目总结 在这次更新中，我们以我们作为Verity生物信息学核心、扩展核心的经验为基础 服务包括自上次申请以来在该领域取得的进展。利用电子医疗进行研究 随着电子病历数据可用性的提高，电子病历(EMR)数据继续增长。同时，方法可用于 利用数据进行研究也取得了进展，包括自然语言处理(NLP)和机器 学习(ML)以提取嵌入到叙述性笔记中的关键临床数据，并将这些数据包括在模型中 疾病风险和结果。然而，在获取原始电子病历数据之间仍存在很大差距 针对计费和患者护理进行了优化，并能够在临床上充分和适当地利用这些数据 研究。通过协商，作为生物信息学核心的一部分提供的课程，以及目前的核心项目， 我们已经确定了临床研究人员的4个高需求和/或未得到满足的需求：(1)使用 电子病历数据；(2)使用NLP从叙述性笔记中提取临床数据，包括这一技术的早期应用 研究健康的社会决定因素的技术；(3)使用电子病历研究治疗效果和 因果推理方法的应用；以及(4)多机构电子病历研究的方法 需要直接共享数据(称为联合学习)。 生物信息学核心的使命仍然是支持儿科和 成人风湿病和肌肉骨骼(MSK)研究社区应用和整合生物信息学 使用电子病历数据进行临床研究的方法。虽然我们的目标受众仍然是实习生和 初级教员，在这次更新中，我们的扩展服务也是为有兴趣的老牌研究人员设计的 将生物信息学纳入他们的研究计划。目标1.为调查人员提供方法 使用来自EMR的信息获得稳健和准确的表型，并将这些数据整合用于临床 学习。这需要应用有监督和无监督的机器学习方法来进行表型分析 电子病历数据。同时，我们将使用应用于EMR数据的因果推断方法来研究治疗 效果。目的2.提供NLP支持，支持使用EMR数据进行临床研究。我们将支持和 发展NLP的使用，将健康的社会决定因素(SDoH)纳入健康公平的研究中 电子病历数据。此外，我们还将支持和教育调查人员使用基于自然语言规划的数据和工具。目标3. 加强风湿病和MSK临床研究之间的现有联系并建立新的合作伙伴关系 生物信息学社区通过核心平台和咨询服务。 生物信息学核心团队和专家顾问网络将进行咨询，提供 教育服务，并向研究社区提供生物信息学研究服务。另外， 我们将在我们的基础上，通过联合学习方法实现跨机构研究 风湿性和肌肉骨骼疾病的临床研究。

PROJECT SUMMARY In this renewal, we build on our experience serving as the VERITY Bioinformatics Core, expanding Core services to include advancements in the field since the last application. Research using electronic medical record (EMR) data continues to grow with increasing availability of EMR data. Simultaneously, methods to utilize data for research have also advanced including natural language processing (NLP) and machine learning (ML) to extract crucial clinical data embedded in narrative notes, and to include these data in models of disease risk and outcomes. However, there remains a large gap between access to raw EMR data optimized for billing and patient care, and the ability to fully and appropriately utilize these data in clinical research. Through consultations, courses offered as part of the Bioinformatics Core, and current Core projects, we have identified 4 areas of high demand and/or unmet need for clinical investigators: (1) phenotyping using EMR data; (2) extraction of clinical data from narrative notes using NLP, including early applications of this technology to study social determinants of health; (3) use of EMR for studies of treatment effects and applications of causal inference methods; and (4) approaches for multi-institutional EMR studies without requiring direct sharing of data (termed federated learning). The mission of the Bioinformatics Core remains supporting investigators from the pediatric and adult rheumatic and musculoskeletal (MSK) research community to apply and integrate bioinformatics approaches to clinical research studies using EMR data. While our target audience remains trainees and junior faculty, in this renewal, our expanded services are also designed for established investigators interested in incorporating bioinformatics to their research programs. Aim 1. To provide methods for investigators to obtain robust and accurate phenotypes using information from EMRs and integrating these data for clinical studies. This requires applying supervised and unsupervised machine learning approaches for phenotyping with EMR data. As well, we will utilize causal inference methods applied to EMR data for studies of treatment effects. Aim 2. To provide NLP support enabling clinical research studies with EMR data. We will support and develop the use of NLP to incorporate social determinants of health (SDoH) in studies of health equity using EMR data. As well, we will support and educate investigators on the use of NLP based data and tools. Aim 3. To strengthen existing ties and build new partnerships between the rheumatic and MSK clinical research and bioinformatics communities through Core platforms and consulting services. The Bioinformatics Core team and a network of expert advisors will perform consultations, provide educational services, and deliver bioinformatics research services to the Research Community. Additionally, we will build on our foundation to enable cross-institutional studies with federated learning approaches for clinical studies on rheumatic and musculoskeletal conditions.