Bionformatics Resource Core

项目摘要 在这次更新中，我们建立在我们作为VERITY生物信息学核心的经验基础上， 服务包括自上次申请以来该领域的进步。电子医疗研究 电子病历（EMR）数据随着EMR数据可用性的增加而持续增长。同时， 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）提取嵌入在叙述性笔记中的关键临床数据，并将这些数据纳入模型 疾病的风险和结果。然而，在获取原始EMR数据和 针对计费和患者护理进行了优化，并能够在临床中充分和适当地利用这些数据 research.通过咨询，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者的4个高需求和/或未满足需求的领域：（1）表型分析， EMR数据;（2）使用NLP从叙述性笔记中提取临床数据，包括早期应用 研究健康的社会决定因素的技术;（3）使用EMR研究治疗效果， 因果推理方法的应用;（4）多机构EMR研究的方法， 需要直接共享数据（称为联邦学习）。 生物信息学核心的使命仍然是支持儿科和 成人风湿和肌肉骨骼（MSK）研究社区应用和整合生物信息学 使用EMR数据进行临床研究的方法。虽然我们的目标受众仍然是学员， 初级教师，在这次更新中，我们扩展的服务也是为有兴趣的既定研究人员设计的 将生物信息学融入到他们的研究计划中。目标1.为研究者提供方法， 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 问题研究这需要应用有监督和无监督的机器学习方法进行表型分析 电子病历数据同时，我们将利用因果推理方法应用于EMR数据的治疗研究 方面的影响.目标二。提供NLP支持，使临床研究能够使用EMR数据。扶持和 发展NLP的使用，将健康的社会决定因素（SDoH）纳入健康公平性研究， 电子病历数据。此外，我们将支持和教育研究人员使用基于NLP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，并为研究社区提供生物信息学研究服务。此外，本发明还 我们将建立在我们的基础上，使跨机构的研究与联邦学习方法， 风湿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.