An in-silico method for epidemiological studies using Electronic Medical Records

使用电子病历进行流行病学研究的计算机方法

• 批准号: 7726747
• 负责人: HUA XU
• 金额: $ 27.33万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-03 至 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7726747
• 关键词: Affect; American Cancer Society; Breast; Cereals; Clinical; Clinical Data; Cohort Studies; Colon Carcinoma; Computer Simulation; Computerized Medical Record; Data; Data Quality; Data Sources; Disease; Epidemiologic Studies; Epidemiology; Health; Hospitals; Human Resources; Informatics; Knowledge; Malignant Neoplasms; Manuals; Methods; NIH Program Announcements; Natural Language Processing; Patients; Population; Prevention; Randomized Controlled Trials; Records; Reporting; Research; Risk Factors; Selection Bias; Statistical Methods; System; Technology; Text; Time; Validation; anticancer research; base; cancer therapy; cancer type; cost; interest; prevent

DESCRIPTION: Observational epidemiological studies are effective methods for identifying factors affecting the health and illness of populations, as well as for determining optimal treatments for diseases, such as cancers. However, conventional epidemiological research usually involves personnel-intensive effort (such as manual chart and public records review) and can be very time consuming before conclusive results are obtained. Recently, a large amount of detailed longitudinal clinical data has been accumulated at hospitals' Electronic Medical Records (EMR) systems and it has become a valuable data source for epidemiological studies. However, there are two obstacles that prevent the wide usage of EMR data in epidemiological studies. First, most of the detailed clinical information in EMRs is embedded in narrative text and it is very costly to extract that information manually. Second, EMRs usually have data quality problems such as selection bias and missing data, which require adaptation of conventional statistical methods developed for randomized controlled trials. In this study, we propose an in silico informatics-based approach for observational epidemiological studies using EMR data. We hypothesize that existing EMR data can be used for certain types of epidemiological studies in a very efficient manner with the help of informatics methods. The informatics-based approach will contain two major components. One is an NLP (Natural Language Processing) based information extraction system that can automatically extract detailed clinical information from EMR and another is a set of statistical and informatics methods that can be used to analyze EMR-derived data. If the feasibility of this approach is proven, it will change the standard paradigm of observational epidemiological research, because it has the capability to answer an epidemiological question in a very short time at a very low cost. The specific aim of this study is to develop an automated informatics approach to extract both fine-grained cancer findings and general clinical information from EMRs and use them to conduct cancer related epidemiological studies. We will perform both casecontrol and cohort studies related to prevention and treatment of breast and colon cancers using EMR data. The informatics approach will be validated on EMRs from two major hospitals to demonstrate its generalizability. Epidemiological findings from our study will be compared to reported findings for validation. Narrative: According to the American Cancer Society, about 7.6 million people died from various types of cancer in the world during 2007. It is very important to identify risk factors of cancers and to determine optimal treatments of cancers, and epidemiological study is one of the methods to achieve it. This proposed study will use natural language processing technologies to automatically extract fine-grained cancer information from existing patient electronic medical records and use it to conduct cancer related epidemiological studies, thus accelerating knowledge accumulation of cancer research. SPECIAL REVIEW NOTE: In order to conform to the scientific objectives outlined in the program announcement RFA-GM-09-008, EUREKA applications submitted to the NCI were initially evaluated by a group of reviewers representing diverse scientific interests. The priority score reflects the average of all the scores given by the full committee after a thorough discussion.

描述：观察性流行病学研究是确定影响人群健康和疾病的因素以及确定癌症等疾病的最佳治疗方法的有效方法。然而，传统的流行病学研究通常需要大量人员（例如手册图表和公共记录审查），并且在获得结论性结果之前可能非常耗时。近年来，医院电子病历（EMR）系统积累了大量详细的纵向临床数据，成为流行病学研究的宝贵数据源。然而，有两个障碍阻碍了电子病历数据在流行病学研究中的广泛使用。首先，电子病历中的大部分详细临床信息都嵌入在叙述性文本中，手动提取这些信息的成本非常高。其次，电子病历通常存在数据质量问题，例如选择偏差和数据缺失，这需要适应为随机对照试验开发的传统统计方法。 在这项研究中，我们提出了一种基于计算机信息学的方法，使用 EMR 数据进行观察性流行病学研究。我们假设现有的电子病历数据可以在信息学方法的帮助下以非常有效的方式用于某些类型的流行病学研究。基于信息学的方法将包含两个主要组成部分。一个是基于 NLP（自然语言处理）的信息提取系统，可以自动从 EMR 中提取详细的临床信息，另一个是一组可用于分析 EMR 衍生数据的统计和信息学方法。如果这种方法的可行性被证明，它将改变观察流行病学研究的标准范式，因为它有能力在很短的时间内以很低的成本回答流行病学问题。本研究的具体目的是开发一种自动化信息学方法，从 EMR 中提取细粒度的癌症发现和一般临床信息，并利用它们进行癌症相关的流行病学研究。我们将使用 EMR 数据进行与乳腺癌和结肠癌预防和治疗相关的病例对照和队列研究。信息学方法将在两家主要医院的电子病历上进行验证，以证明其普遍性。我们的研究的流行病学结果将与报告的结果进行比较以进行验证。 叙述：根据美国癌症协会的统计，2007 年全球约有 760 万人死于各种类型的癌症。识别癌症的危险因素非常重要。 癌症并确定癌症的最佳治疗方法，流行病学研究是 实现它的方法之一。这项拟议的研究将使用自然语言 自动提取细粒度癌症信息的处理技术 现有患者电子病历并利用其进行癌症相关 流行病学研究，从而加速癌症研究的知识积累。 特别审查说明：为了符合计划公告 RFA-GM-09-008 中概述的科学目标，提交给 NCI 的 EUREKA 申请最初由代表不同科学兴趣的一组审查员进行了评估。优先级分数反映了全体委员会经过充分讨论后给出的所有分数的平均值。