Evidence-based Diagnostic Tools for Translational and Clinical Research (eTfor2)

描述(由申请者提供)：eTfor2项目将开发和评估开源程序和知识表示法，以更好地为翻译和临床研究确定患者的特征。该项目涉及国家医学图书馆(NLM)的信息和知识处理，包括自然语言处理和文本摘要，(B)将现象信息和基因组信息联系起来的方法，以及(C)整合来自不同来源的信息。翻译研究将临床患者描述符(PINOME)与基因组研究的结果相关联，例如全基因组关联研究(GWAS)。定义表型的标准方法需要昂贵的、劳动密集型的队列登记，以识别患有疾病的患者和适当的对照。最近，翻译和临床研究人员使用电子病历(EMR)数据作为识别患者特征的替代方案。然而，由于ICD9帐单代码固有的不准确性，电子病历案例提取需要大量的人工审查和案例选择的“调整”。虽然促进EMR文本提取的相关和有用的自然语言处理(NLP)方法激增，但这些方法使用的目标患者描述符通常仍然是非标准的和本地定义的，并且因疾病、项目和机构的不同而不同。此类NLP应用程序充其量使用标准术语描述符(如SNOMED-CT)作为EMR提取目标。然而，没有普遍使用的“标准”知识库将这种“可提取的”描述符链接到学术质量的知识来源，详细说明在每种疾病中出现了哪些可靠的发现。为了促进翻译和临床研究，eTfor2项目将提供一个开源的、基于证据的电子临床知识库(KB)和相关的NLP工具，使任何地点的研究人员都能够在发现和疾病水平上提取一组标准的基于EMR的现象描述符。它将进一步包括诊断决策支持逻辑，以确认患者在EMR记录中对诊断的支持程度。ETfor2项目将减少为临床和翻译研究收集EMR患者描述符所需的工作，并使新的翻译工作能够在发现和疾病水平上识别基因组关联。ETfor2资源应该提高基于EMR的翻译和临床研究的质量和跨机构有效性。

DESCRIPTION (provided by applicant): The eTfor2 project will develop and evaluate open-source programs and knowledge representations to better characterize patients for translational and clinical research studies. The project addresses National Library of Medicine (NLM) RFA initiatives for: (a) information & knowledge processing, including natural language processing and text summarization, (b) approaches for linking phenomic and genomic information, and (c) integration of information from heterogeneous sources. Translational studies correlate clinical patient descriptors (phenome) with results of genomic investigations, e.g., genome-wide association studies (GWAS). Standard methods for defining phenotypes require costly, labor-intensive cohort enrollments to identify patients with diseases and appropriate controls. Recently, translational and clinical researchers have used electronic medical record (EMR) data as an alternative to identifying patient characteristics. However, EMR case extraction requires substantial manual review and "tuning" for case selection, due to the inaccuracies inherent in ICD9 billing codes. While relevant and useful natural language processing (NLP) approaches to facilitate EMR text extraction have proliferated, the target patient descriptors these approaches employ typically remain non-standard and locally defined, and vary from disease to disease, project to project and institution to institution. At best, such NLP applications use standard terminology descriptors such as SNOMED-CT as EMR extraction targets. Yet, there is no generally utilized "standard" knowledge base that links such "extractable" descriptors to an academic-quality knowledge source detailing what findings have been reliably reported to occur in each disease. To facilitate translational and clinical research, the eTfor2 project will make available an open-source, evidence-based, electronic clinical knowledge base (KB) and related NLP tools enabling researchers at any site to extract a standard "target" set of EMR-based phenomic descriptors at both the finding and disease levels. It will further include diagnostic decision support logic to confirm the degree of support for patients' diagnoses in their EMR records. The eTfor2 project will decrease effort required to harvest EMR patient descriptors for clinical and translational studies, and enable new translational work that identifies genomic associations at both finding and disease levels. The eTfor2 resources should improve the quality and cross-institutional validity of EMR-based translational and clinical studies.