Bridging the Semantic Gap Between Research Eligibility Criteria and Clinical Data

弥合研究资格标准和临床数据之间的语义差距

基本信息

批准号：
7653874
负责人：
CHUNHUA WENG
金额：
$ 35.79万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-04-01 至 2012-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7653874
关键词：
Clinical Clinical Data Clinical Research Clinical Trials Code Complex Computers Creatinine Data Databases Drug Formulations Electronic Health Record Eligibility Determination Enrollment Female Goals Guidelines Health Hour Human Kidney Kidney Failure Knowledge Link Manuals Medical Medicine Methods Natural Language Processing Ontology Participant Patient Recruitments Patients Phenotype Population Surveillance Positioning Attribute Problem Solving Procedures Process Protocols documentation Reporting Research Science Screening procedure Semantics Serum Signs and Symptoms Source System Techniques Terminology Text Time Translating Translations Universities Work abstracting base biomedical informatics clinical phenotype clinical practice cost data mining design effective therapy eligible participant experience improved information organization knowledge base natural language novel repository skills social statistics

项目摘要

DESCRIPTION (provided by applicant): Our long-term objective is to enlarge the scope and efficiency of clinical research through enhanced use of clinical data to support clinical research decisions. This proposal aims to improve the use of electronic health records (EHR) to automate clinical trials eligibility screening by developing a new semantic alignment framework. Clinical trials research is an important step for translating breakthroughs in basic biomedical sciences into knowledge that will benefit clinical practice and human health. However, a significant obstacle is identifying eligible participants. Eighty-six percent of all clinical trials are delayed in patient recruitment for from one to six months and 13% are delayed by more than six months. Enrollment delay is expensive. In a recent large, multi-center trial, about 86.8 staff hours and more than $1000 was spent to enroll each participant. Ineffective enrollment also produces a big social cost in that up to 60% of patients can miss being identified. The broad deployment of EHR systems has created unprecedented opportunities to solve the problem because EHR systems contain a rich source of information about potential participants. However, it is often a knowledge-intensive, time-consuming, and inefficient manual procedure to match eligibility criteria such as "renal in- sufficiency" to clinical data such as "serum creatinine = 1.0 mg/dl for an 80-year old white female patient." This enduring challenge is partly caused by the disconnection between abstract and ambiguous eligibility criteria and highly specific clinical data manifestations; we call this a semantic gap. Despite earlier work on computer-based clinical guidelines and protocols, limited effort has been devoted to support automatic matching between concepts and their manifestations in patient phenotypes such as signs and symptoms. We hypothesize that we can characterize the semantic gap and design a knowledge-based, natural-language processing assisted semantic alignment framework to bridge the semantic gap. Therefore, our specific aims are: (1) to investigate the semantic gap between clinical trials eligibility criteria and clinical data; (2) to design a concept-based, computable knowledge representation for eligibility criteria; (3) to design a semantic alignment framework linking an eligibility criteria knowledge base and a clinical data warehouse to generate semantic queries for eligibility identification; and (4) to evaluate the utility of the semantic alignment framework. This research is novel and unique in that (1) there are no prior studies about the semantic gap between eligibility criteria and clinical data; and (2) for the first time, we design a semantic alignment framework to automatically match eligibility criteria to clinical data. The research team comprising expertise from the Department of Biomedical Informatics at Columbia University and the Division of General Medicine from UCSF are uniquely positioned to carry out this research, given the experience of the team (medical knowledge representation, natural language processing, controlled clinical terminology, ontology-based semantic reasoning, data mining, statistics, health data organization, semantic harmonization, and clinical trials), the availability of a repository of 13 years of data on 2 million patients, and the availability of a natural language processor called MedLEE to convert millions of narrative reports into richly coded clinical data.

描述（由申请人提供）：我们的长期目标是通过加强临床数据的使用来支持临床研究决策，从而扩大临床研究的范围和效率。该提案旨在通过开发新的语义对齐框架来改进电子健康记录 (EHR) 的使用，以实现临床试验资格筛选的自动化。临床试验研究是将基础生物医学科学的突破转化为有益于临床实践和人类健康的知识的重要一步。然而，一个重大障碍是确定合格的参与者。 86% 的临床试验患者招募延迟一到六个月，13% 延迟超过六个月。延迟注册的代价是昂贵的。在最近的一项大型多中心试验中，招募每位参与者花费了大约 86.8 个小时的工作时间和 1000 多美元。无效的登记还会产生巨大的社会成本，因为高达 60% 的患者可能会错过识别。 EHR 系统的广泛部署为解决该问题创造了前所未有的机会，因为 EHR 系统包含有关潜在参与者的丰富信息源。然而，将“肾功能不全”等资格标准与“80 岁白人的血清肌酐 = 1.0 mg/dl”等临床数据进行匹配通常是一种知识密集型、耗时且低效的手动程序。女病人。”这一持久的挑战部分是由于抽象和模糊的资格标准与高度具体的临床数据表现之间的脱节造成的；我们称之为语义差距。尽管早期在基于计算机的临床指南和方案方面开展了工作，但在支持概念与其在患者表型（例如体征和症状）中的表现之间的自动匹配方面所做的努力仍然有限。我们假设我们可以表征语义差距并设计一个基于知识的、自然语言处理辅助的语义对齐框架来弥合语义差距。因此，我们的具体目标是：（1）调查临床试验资格标准与临床数据之间的语义差距； (2) 为资格标准设计基于概念的、可计算的知识表示； (3) 设计一个连接资格标准知识库和临床数据仓库的语义对齐框架，以生成用于资格识别的语义查询； (4)评估语义对齐框架的效用。这项研究的新颖性和独特之处在于（1）之前没有关于资格标准和临床数据之间语义差距的研究； (2)我们首次设计了一个语义对齐框架来自动将资格标准与临床数据相匹配。鉴于团队的经验（医学知识表示、自然语言处理、受控临床术语、基于本体的语义推理、数据挖掘、统计、健康数据组织、语义协调和临床试验）、包含 200 万患者 13 年数据的存储库的可用性，以及名为 MedLEE 的自然语言处理器的可用性将数百万份叙述性报告转换为编码丰富的临床数据。