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Natural language processing for clinical and translational research

用于临床和转化研究的自然语言处理

基本信息

批准号：
9033918
负责人：
HONGFANG LIU
金额：
$ 56.28万
依托单位：
MAYO CLINIC ROCHESTER
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-04-01 至 2018-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9033918
关键词：
Acceleration Adopted Adoption Adverse drug effect Algorithms Architecture Attention Clinical Clinical Investigator Clinical Research Code Communities Computerized Medical Record DNA Databases Data Data Set Development Dictionary Discipline of Nursing Disease Documentation Elements Exclusion Criteria Genes Genomics Goals Growth Health Informatics Institution Knowledge Link Logical Observation Identifiers Names and Codes Manuals Medical Education Modeling Natural Language Processing Onset of illness Output Patients Pharmacogenomics Phenotype Play Process Radiology Specialty Reporting Research Research Personnel Research Project Grants Resources Role SNOMED Clinical Terms Semantics Shoes Site Statutes and Laws Structure Syndrome System Technology Text Translational Research Treatment outcome Work base clinical application clinical care clinical phenotype computer human interaction data exchange data modeling experience financial incentive flexibility human centered computing interoperability novel open source patient safety rapid growth success tool usability user-friendly

项目摘要

DESCRIPTION (provided by applicant): Rapid growth in the clinical implementation of large electronic medical records (EMRs) has led to an unprecedented expansion in the availability of dense longitudinal datasets for clinical and translational research. This growth is being fueled by recent federal legislation that provides generous financial incentives to institutions demonstrating aggressive application and "meaningful use" of comprehensive EMRs. Efforts are already underway to link these EMRs across institutions, and standardize the definition of phenotypes for large scale studies of disease onset and treatment outcome, specifically within the context of routine clinical care. However, a well-known challenge for secondary use of EMR data for clinical and translational research is that much of detailed patient information is embedded in narrative text. Natural Language Processing (NLP) technologies, which are able to convert unstructured clinical text into coded data, have been introduced into the biomedical domain and have demonstrated promising results. Researchers have used NLP systems to identify clinical syndromes and common biomedical concepts from radiology reports, discharge summaries, problem lists, nursing documentation, and medical education documents. Different NLP systems have been developed at different institutions and utilized to convert clinical narrative text into structured data that may be used for other clinical applications and studies. Successful stories in applying NLP to clinical and translational research have been reported widely. However, institutions often deploy different NLP systems, which produce various types of output formats and make it difficult to exchange information between sites. Therefore, the lack of interoperability among different clinical NLP systems becomes a bottleneck for efficient multi-site studies. In addition, many successful studies often require a strong interdisciplinary team where informaticians and clinicians have to work very closely to iteratively define optimal algorithms for clinical phenotypes. As intensive informatics support may not be available to every clinical researcher, the usability of NLP systems for end users is another important issue. The proposed project builds upon first-hand knowledge and experience across the research team in the use of NLP for clinical and translational research projects. There are several big informatics initiatives for clinical and translational research but those initiatives generally assume one shoe fits all and follow top-down approaches to develop NLP solutions. Complementary to those initiatives, we will use a bottom-up approach to handle interoperability and usability: i) we will obtain a common NLP data model and exchange format through empirical analysis of existing NLP systems and NLP results; ii) we will develop a user-centric NLP front end interface for NLP systems wrapped to be consistent with the proposed NLP data model and exchange format incorporating usability analysis into the agile development process. All deliverables will be distributed through the open health NLP (OHNLP) consortium which we intend to make it more open and inclusive.

描述（由申请人提供）：大型电子病历（emr）临床应用的快速增长导致了临床和转化研究密集纵向数据集可用性的前所未有的扩展。这种增长是由

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Clinical decision support with automated text processing for cervical cancer screening.

临床决策支持具有自动化文本处理，用于宫颈癌筛查。

DOI：
10.1136/amiajnl-2012-000820
发表时间：
2012-09
期刊：
Journal of the American Medical Informatics Association : JAMIA
影响因子：
0
作者：
Wagholikar KB;MacLaughlin KL;Henry MR;Greenes RA;Hankey RA;Liu H;Chaudhry R
通讯作者：
Chaudhry R

ADEpedia 2.0: Integration of Normalized Adverse Drug Events (ADEs) Knowledge from the UMLS.

ADEpedia 2.0：整合来自 UMLS 的标准化药物不良事件 (ADE) 知识。

DOI：
发表时间：
2013
期刊：
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
影响因子：
0
作者：
Jiang,Guoqian;Liu,Hongfang;Solbrig,HaroldR;Chute,ChristopherG
通讯作者：
Chute,ChristopherG

CATTLE (CAncer treatment treasury with linked evidence): An integrated knowledge base for personalized oncology research and practice.

CATTLE（具有关联证据的癌症治疗宝库）：用于个性化肿瘤学研究和实践的综合知识库。

DOI：
10.1002/psp4.12174
发表时间：
2017
期刊：
CPT: pharmacometrics & systems pharmacology
影响因子：
0
作者：
Soysal,E;Lee,H-J;Zhang,Y;Huang,L-C;Chen,X;Wei,Q;Zheng,W;Chang,JT;Cohen,T;Sun,J;Xu,H
通讯作者：
Xu,H

Assessing the Need of Discourse-Level Analysis in Identifying Evidence of Drug-Disease Relations in Scientific Literature