Discovering and Applying Knowledge in Clinical Databases

发现和应用临床数据库中的知识

• 批准号: 8729506
• 负责人: GEORGE M HRIPCSAK
• 金额: $ 42.47万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-04-01 至 2018-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8729506
• 关键词: Accounting; Address; Admission activity; Algorithms; Area; Award; Chronic Disease; Clinical; Clinical Informatics; Clinical Research; Correlation Studies; Data; Data Collection; Databases; Dictionary; Electronic Health Record; Engineering; Etiology; Event; Fruit; Goals; Health; Healthcare; Inequality; Information Resources; Information Theory; Knowledge; Learning; Longitudinal Studies; Measurement; Measures; Medical; Methods; Modeling; Noise; Observational Study; Ontology; Performance; Phenotype; Population Heterogeneity; Privacy; Process; Publications; Publishing; Rare Diseases; Research; Research Personnel; Resources; Sampling Biases; Solutions; Systematic Bias; Terminology; Time; Time Series Analysis; Unified Medical Language System; Work; abstracting; clinical phenotype; computerized data processing; data mining; data modeling; improved; innovation; interest; interoperability; knowledge base; meetings

DESCRIPTION (provided by applicant): The long term goal of our ongoing project, "Discovering and applying knowledge in clinical databases," is to learn from data in the electronic health record (EHR) and to apply that knowledge to relevant problems. The advent of the electronic health record (EHR) greatly amplifies the ability to carry out observational research, opening the possibility of covering emerging problems, diverse populations, rare diseases, and chronic diseases in long-term longitudinal studies. Unfortunately, the EHR carries additional challenges. We believe that the biggest challenge comes from the inaccuracy, incompleteness, complexity, and resulting bias inherent in the recording of the health care process. Put another way, EHR data are not simply research data with more noise and missing some values; instead the EHR carries systematic biases that must be addressed before the data can reach their potential. We propose to characterize the effects of the health care process on EHR data, to enumerate the potential biases, and to provide mechanisms to circumvent them. In effect, we propose to study the EHR as an object of interest in itself, using new models, data mining, existing knowledge bases, and innovative algorithms to better understand EHR biases so that we can identify them and correct them or avoid them. We include expertise from two of the nation's major phenotyping projects, eMERGE and OMOP. We hypothesize that we can learn about biases due to the health process through data mining and knowledge engineering and that we can correct or at least avoid those biases, enabling us to better answer informatics and clinical questions. Our aims are as follows: (1) Study health care process biases by correlating raw EHR variables with a panel of health care process-related variables (e.g., admission), using lagged correlation to account for temporal effects, and populating a health care process resource with the correlations and observations. (2) Find associations among raw EHR variables using lagged correlation, information theory, Granger causality, and temporally ordered N-tuples of events, correcting for the health care process biases discovered in Aim 1. (3) Facilitate the definition of higher-level clinical phenotype concepts by applying knowledge resources-including eMERGE and OMOP phenotype definitions and ontologies such as our Medical Entities Dictionary and the UMLS-to the fruit of Aims 1 and 2 to produce semi-automated and automated phenotype query definitions. (4) Develop a high-throughput method to validate phenotype definitions by measuring the ability to uncover known associations, use the generated phenotypes and associations to answer clinical questions, and disseminate the results, including a large knowledge base of correlations that can be used by other researchers to conduct their own studies.

描述（由申请人提供）：我们正在进行的项目“发现和应用临床数据库中的知识”的长期目标是从电子健康记录（EHR）中的数据中学习，并将这些知识应用于相关问题。电子健康记录（EHR）的出现极大地增强了进行观察性研究的能力，为在长期纵向研究中涵盖新出现的问题、不同人群、罕见病和慢性病提供了可能性。不幸的是，EHR带来了额外的挑战。我们认为，最大的挑战来自医疗保健过程记录中固有的不准确性、不完整性、复杂性和由此产生的偏见。换句话说，EHR数据不仅仅是具有更多噪音和缺失某些值的研究数据;相反，EHR带有系统性偏见，必须在数据发挥潜力之前解决这些偏见。我们建议的特点的EHR数据的医疗保健过程中的影响，列举潜在的偏见，并提供机制来规避它们。实际上，我们建议将EHR本身作为感兴趣的对象进行研究，使用新模型，数据挖掘，现有知识库和创新算法来更好地理解EHR偏见，以便我们可以识别它们并纠正它们或避免它们。我们包括来自两个国家的主要表型项目，eMERGE和OMOP的专业知识。 我们假设，我们可以通过数据挖掘和知识工程来了解由于健康过程而产生的偏见，并且我们可以纠正或至少避免这些偏见，使我们能够更好地回答信息学和临床问题。我们的目标如下：（1）通过将原始EHR变量与一组医疗保健过程相关变量（例如，入院），使用滞后相关性来说明时间效应，以及用相关性和观察来填充健康护理过程资源。(2)使用滞后相关性、信息论、格兰杰因果关系和事件的时间顺序N元组来查找原始EHR变量之间的关联，纠正目标1中发现的医疗保健过程偏差。(3)通过将知识资源（包括eMERGE和OMOP表型定义和本体，例如我们的医学实体词典和UMLS）应用于目标1和2的成果，以生成半自动化和自动化表型查询定义，促进更高级别临床表型概念的定义。(4)开发一种高通量方法，通过测量发现已知关联的能力来验证表型定义，使用生成的表型和关联来回答临床问题，并传播结果，包括可供其他研究人员进行自己研究的相关性的大型知识库。