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: 8110041
• 负责人: HUA XU
• 金额: $ 25.23万
• 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/8110041
• 关键词: Affect; American Cancer Society; Breast Cancer Treatment; 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; 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; 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.

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涉及人员密集的工作(如手动图表和公共记录审查)，并且在获得决定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来源。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，急诊磁共振中的大多数详细临床信息都嵌入到叙述性文本中，手动提取这些信息的成本非常高。其次，EMR通常存在数据质量问题，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电子信息学的方法，用于使用电子病历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。以信息学为基础的方法将包含两个主要组成部分。一种是基于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；另一种是一套统计和信息学方法，可以用于分析EMR派生的数据。如果这种方法的可行性得到证明，它将改变观察性流行病学研究的标准范式，因为它有能力在非常短的时间内以非常低的成本回答流行病学问题。这项研究的具体目的是开发一种自动化的信息学方法，从急诊医生中提取细粒度的癌症发现和一般临床信息，并使用它们进行与癌症相关的流行病学研究。我们将使用EMR数据进行与预防和治疗乳腺癌和结肠癌有关的病例对照和队列研究。信息学方法将在来自两家主要医院的急救人员身上进行验证，以证明其普适性。我们研究的流行病学结果将与报道的结果进行比较，以进行验证。