Capturing and linking genomic and clinical information

描述(由申请人提供)：该项目的长期目标是使用自然语言处理(NLP)来构建一个高通量工具，通过自动从电子病历(EMR)和期刊文章中提取和组织临床和遗传信息来促进癌症研究。我们的研究涉及先进的NLP技术，以：1)能够挖掘EMR中的表型和基因数据；2)自动从期刊中积累与癌症和生物分子关系有关的知识；3)为研究人员开发一个网络使能的可视化工具，将呈现知识的不同观点；以及4)开发一个基础设施，将链接到纽约长老会医院(NYPH)的临床数据仓库和GeneWays，这是一个相关项目，允许研究人员可视化路径。 更具体地说，MedLEE(我们开发的从EMR中提取和编码临床和环境信息的NLP系统)将扩展到提取EMR中包含的遗传信息；随后，将处理12年的患者报告并将提取的数据添加到仓库中。此外，将在MedLEE和GeneWays(其中包含我们开发的另一个从期刊文章中提取和编码生物分子关系的NLP系统)的基础上开发一个新的系统，PhenoGenes。PhenoGenes将捕捉与癌症治疗、诊断和预后直接相关的生物分子相互作用。它还将生成一个可扩展标记语言知识库，它将整合和组织将要捕获的信息，以及一个使用户能够浏览和查看根据不同方向(例如，基因、疾病、组织、相互作用等)聚在一起的知识的网络工具。知识库将链接到GeneWays系统，以便可以可视化相关路径。 MedLEE已在NYPH投入使用。事实也证明，这两种NLP系统都是非常有效的。目前的项目建立在我们在这些系统上的经验和成功的基础上。相关兼容的临床和生物分子NLP系统的可获得性提供了一个特殊的机会，为捕获、集成和组织表型和基因数据和知识铺平道路，这些数据和知识将用于从根本上改善患者护理。

DESCRIPTION (provided by applicant): The long-term aim of this project is to use natural language processing (NLP) to build a high throughput tool for facilitating cancer research by automatically extracting and organizing clinical and genetic information from the Electronic Medical Record (EMR) and from journal articles. Our research involves advanced NLP techniques to: 1) enable the mining of phenotypic and genotypic data in the EMR; 2) automatically amass knowledge concerned with cancer and biomolecular relationships from journals; 3) develop a WEB-enabled visualization tool for researchers that will present diverse views of the knowledge; and 4) develop an Infrastructure that will link to the clinical data warehouse at New York Presbyterian Hospital (NYPH) and to GeneWays, a related project that allows researchers to visualize pathways. More specifically, MedLEE (the NLP system we developed that extracts and encodes clinical and environmental information from the EMR) will be extended to extract genetic information contained in the EMR; subsequently, twelve years of patient reports will be processed and the extracted data added to the warehouse. In addition, a new system, PhenoGenes, will be developed based on MedLEE and GeneWays (which contains another NLP system we developed that extracts and codifies biomolecular relations from journal articles). PhenoGenes will capture biomolecular interactions directly associated with the treatment, diagnosis, and prognosis of cancer. It will also generate an XML knowledge base that will integrate and organize the information that will be captured, and a Web-enabled tool that will allow users to browse and view the knowledge clustered according to different orientations (e.g. gene, disease, tissue, interaction, etc.). The knowledge base will be linked to the GeneWays system, so that relevant pathways can be visualized. MedLEE is utilized operationally at NYPH. It also has been demonstrated that both NLP systems are highly effective. This current project builds upon our experience and success with these systems. The availability of related compatible clinical and biomolecular NLP systems, provide an exceptional opportunity to pave the way for capture, integration and organization of phenotypic and genotypic data and knowledge that will be used to radically improve patient care.