Capturing and linking genomic and clinical information

描述（由申请人提供）：该项目的长期目标是利用自然语言处理（NLP）建立一个高通量工具，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，促进癌症研究。我们的研究涉及先进的自然语言处理技术：1)能够在EMR中挖掘表型和基因型数据；2)从期刊上自动积累有关癌症和生物分子关系的知识；3)为研究人员开发一个基于web的可视化工具，展示不同的知识观点；4)开发一个基础设施，连接纽约长老会医院（New York Presbyterian Hospital， NYPH）的临床数据仓库和GeneWays，后者是一个相关的项目，可以让研究人员将路径可视化。

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.