SCH: INT: Collaborative Research: High-throughput Phenotyping on Electronic Health Records using Multi-Tensor Factorization

SCH：INT：协作研究：使用多张量分解对电子健康记录进行高通量表型分析

• 批准号: 1418504
• 负责人: Bradley Malin
• 金额: $ 69.3万
• 依托单位: Vanderbilt University Medical Center
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1418504&HistoricalAwards=false
• 关键词: SCH; INT; Collaborative; Research; throughput

As the adoption of electronic health records (EHRs) has grown, EHRs are now composed of a diverse array of data, including structured information (e.g., diagnoses, medications, and lab results), molecular sequences, unstructured clinical progress notes, and social network information. There is mounting evidence that EHRs are a rich resource for clinical research, but they are notoriously difficult to leverage because of their orientation to healthcare business operations, heterogeneity across commercial systems, and high levels of missing or erroneous entries. Moreover, the interactions among different data sources within an EHR are challenging to model, hampering our ability to leverage traditional analytic frameworks. In recognition of this problem, various efforts have been undertaken to transform EHR data into concise and meaningful concepts, or phenotypes. Yet, to date, these efforts have been ad hoc and labor intensive, resulting in specific phenotypes for specific environments; e.g., type 2 diabetes in the EHR system at Vanderbilt University Medical Center (VUMC). There is an urgent need for scalable phenotyping methods, but several major challenges must be addressed, including: a) patient representation, b) high-throughput phenotype generation from EHRs, c) expert-guided phenotype refinement, and d) phenotype adaptation across institutions. The goal of this project is to address these challenges by developing a general computational framework for transforming EHR data into meaningful phenotypes with only modest levels of expert guidance. The PIs will develop novel courses on Healthcare Analytics as a Massive Open Online Course (MOOC) that covers cross-disciplinary topics at the confluence of computer science and medical informatics, while embellishing existing graduate courses on biomedical informatics. The PIs plan to deliver tutorials and organize workshops at relevant computer science and medical informatics conferences with the goal of sharing research results and developing a community. The PIs will develop outreach modules that focus on freshmen and under-represented students, as well as educational sessions for clinical researchers who are currently performing phenotyping in academic medical centers. Thus, the project has a significant component the integrates research and education as well as providing for new scientific insights.In support of this goal, the team plans to represent and analyze EHR data as inter-connected high-order relations i.e. tensors (e.g. tuples of patient-medication-diagnosis, patient-lab, and patient-symptoms). The proposed analytic framework generalizes several existing data mining methodologies, including dimensionality reduction, topic modeling and co-clustering, which all arise as limited special cases of analyzing second order tensors. It will also enable flexible refinement of candidates to adapt phenotypes from one healthcare institution to another, and will incorporate feedback from domain experts. The accompanying suite of algorithms and methods will enable the automation of high-throughput phenotype generation, refinement, adaptation and applications, in a broad range of health informatics settings and across multiple institutions. This project will integrate biomedical informaticists, computer scientists, and clinical experts. The significance of the resulting phenotypes in diverse clinical applications, including: a) cohort construction, where case and control patients are identified with respect to specific phenotype combinations; b) genome wide association studies (GWAS), where target phenotypes of patients are tested against DNA sequence variation for significant statistical associations; and c) clinical predictive modeling, where a model is developed to predict target phenotypes or diseases will be demonstrated. The framework will be developed with public accessible data from MIMIC-II and CMS and validate in real clinical environments at Northwestern Memorial Hospital and VUMC through several high-impact disease targets (including hypertension, type 2 diabetes, hypothyroidism, atrial fibrillation, rheumatoid arthritis, and multiple sclerosis). Additionally, the methodologies developed through this project will be integrated into existing software platforms that support the representation of EHR-derived phenotypes, but lack a data-driven component for the generation and refinement of candidates. Overall, the proposed framework is expected to have a major impact on translational clinical research including clinical trial design, predictive modeling, epidemiology studies and clinical decision support.

随着电子健康记录(EHR)的普及，EHR现在由各种各样的数据组成，包括结构化信息(例如，诊断、药物和实验室结果)、分子序列、非结构化临床进展笔记和社交网络信息。越来越多的证据表明，电子病历是临床研究的丰富资源，但众所周知，它们很难利用，因为它们面向医疗保健业务运营，跨商业系统的异质性，以及高度缺失或错误的条目。此外，EHR中不同数据源之间的交互建模具有挑战性，阻碍了我们利用传统分析框架的能力。认识到这一问题，已采取各种努力将电子健康记录数据转化为简明和有意义的概念或表型。然而，到目前为止，这些努力是临时的和劳动密集型的，导致了特定环境下的特定表型；例如，范德比尔特大学医学中心(VUMC)EHR系统中的2型糖尿病。迫切需要可扩展的表型方法，但必须解决几个主要挑战，包括：a)患者代表性，b)从EHR高通量产生表型，c)专家指导的表型改进，以及d)跨机构的表型适应。该项目的目标是通过开发一个通用的计算框架，将电子病历数据转化为有意义的表型，只需适度的专家指导，以应对这些挑战。PIS将开发新的医疗保健分析课程，作为一门大型在线开放课程(MOOC)，涵盖计算机科学和医学信息学交汇的跨学科主题，同时完善现有的生物医学信息学研究生课程。PIS计划在相关的计算机科学和医学信息学会议上提供教程和组织研讨会，目的是分享研究成果和发展社区。PIS将开发以新生和代表性不足的学生为重点的外展模块，以及为目前在学术医学中心进行表型分析的临床研究人员举办的教育课程。因此，该项目有一个重要的组成部分，整合了研究和教育，并提供了新的科学见解。为了支持这一目标，该团队计划将EHR数据表示和分析为相互关联的高阶关系，即张量(例如，患者-药物-诊断、患者-实验室和患者-症状的元组)。所提出的分析框架概括了现有的几种数据挖掘方法，包括降维、主题建模和共同聚类，这些方法都是分析二阶张量的有限特例。它还将使候选人能够灵活地精炼，以适应从一个医疗机构到另一个医疗机构的表型，并将纳入领域专家的反馈。随附的一套算法和方法将在广泛的健康信息学环境中和跨多个机构实现高通量表型生成、改进、适应和应用的自动化。该项目将整合生物医学信息学家、计算机科学家和临床专家。所得到的表型在不同的临床应用中的重要性，包括：a)队列构建，其中根据特定的表型组合来确定病例和对照患者；b)全基因组关联研究(Gwas)，其中对照DNA序列变异对患者的目标表型进行测试，以获得显著的统计学关联；以及c)临床预测建模，其中将展示开发用于预测目标表型或疾病的模型。该框架将利用MIMIC-II和CMS的公共可访问数据开发，并在西北纪念医院和VUMC的真实临床环境中通过几个高影响的疾病目标(包括高血压、2型糖尿病、甲状腺功能减退、房颤、类风湿性关节炎和多发性硬化症)进行验证。此外，通过该项目开发的方法将被整合到现有软件平台中，这些软件平台支持电子病历衍生表型的表示，但缺乏用于生成和完善候选者的数据驱动的组成部分。总体而言，拟议的框架预计将对转化性临床研究产生重大影响，包括临床试验设计、预测性建模、流行病学研究和临床决策支持。