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

• 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
• 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

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增强使用临床数据来支持临床研究决策来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一致性框架来提高电子健康记录（EHR）的使用来自动化临床试验资格筛选。临床试验研究是将基本生物医学科学的突破性转化为有益于临床实践和人类健康的知识的重要步骤。但是，一个重要的障碍是确定合格的参与者。所有临床试验中的86％的患者招募延迟了1到六个月，而13％的临床试验延迟了六个月以上。入学延迟很昂贵。在最近的一次大型多中心试验中，大约86.8个员工小时，每位参与者都花了1000多美元。无效的入学率还会产生巨大的社会成本，因为多达60％的患者可能会错过。 EHR系统的广泛部署已经创造了空前的机会来解决该问题，因为EHR系统包含有关潜在参与者的丰富信息来源。但是，这通常是一种知识密集，耗时且效率低下的手动程序，可以符合资格标准，例如“肾脏不足”与临床数据，例如“ 80岁的白人女性患者的血清肌酐= 1.0 mg/dl”。这种持久的挑战部分是由于抽象和模棱两可的资格标准与高度特定的临床数据表现之间的断开引起的。我们将其称为语义差距。尽管较早地研究了基于计算机的临床准则和方案，但仍在努力支持概念及其在患者表型（例如体征和症状）中的表现之间的自动匹配。 我们假设我们可以表征语义差距，并设计基于知识的自然语言处理辅助语义对齐框架，以弥合语义差距。因此，我们的具体目的是：（1）研究临床试验资格标准和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知识表示； （3）设计一个链接资格标准知识库和临床数据仓库的语义一致性框架，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。 这项研究是新颖和独特的，因为（1）没有关于资格标准和临床数据之间语义差距的先前研究。 （2）我们首次设计了一个语义对齐框架，以自动将资格标准与临床数据匹配。鉴于团队的经验，哥伦比亚大学生物医学信息学系的专业知识和UCSF的普通医学部的专业知识是独特的，可以进行这项研究（医学知识表示，自然语言处理，受控的临床术语，基于本体学的语义推理，基于本体的语义推理，数据挖掘，数据挖掘，数据挖掘，数据组织，数据组织，临床范围，临床上，临床上的临床，临床，临床上的临床范围，临床范围，临床上的临床范围） 200万患者，以及称为Medlee的自然语言处理器，将数百万个叙事报告转换为编码丰富的临床数据。