Bridging the Semantic Gap Between Research Eligibility Criteria and Clinical Data

描述（由申请人提供）： 我们的长远目标是透过加强使用临床数据以支持临床研究决策，扩大临床研究的范围和效率。该提案旨在通过开发一个新的语义对齐框架来改进电子健康记录（EHR）的使用，以自动化临床试验资格筛选。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。在所有临床试验中，86%的患者招募延迟了1至6个月，13%的患者招募延迟了6个月以上。延迟注册是昂贵的。在最近的一项大型多中心试验中，大约花费了86.8个工时和1000多美元来招募每位参与者。无效的登记也产生了巨大的社会成本，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这一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，将合格标准如“肾功能不全”与临床数据如“80岁白色女性患者血清肌酐= 1.0 mg/dl”匹配通常是知识密集、耗时且低效的手动程序。“这种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;我们称之为语义鸿沟。尽管早期的工作基于计算机的临床指南和协议，有限的努力一直致力于支持自动匹配的概念和他们的表现在病人的表型，如体征和症状。 我们假设，我们可以描述的语义差距，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，以弥合语义差距。因此，我们的具体目标是：（1）研究临床试验合格标准和临床数据之间的语义鸿沟;（2）设计一个基于概念的、可计算的合格标准知识表示;（3）设计一个连接合格标准知识库和临床数据仓库的语义对齐框架，以生成用于合格性识别的语义查询;以及（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）之前没有关于资格标准和临床数据之间的语义差距的研究;（2）我们首次设计了一个语义对齐框架来自动匹配资格标准和临床数据。研究团队由来自哥伦比亚大学生物医学信息学系和加州大学旧金山分校全科医学系的专业人员组成，具有独特的优势来开展这项研究，因为该团队拥有丰富的经验（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、统计学、健康数据组织、语义协调和临床试验），一个拥有200万患者13年数据的数据库的可用性，以及一个名为MedLEE的自然语言处理器的可用性，它可以将数百万份叙述性报告转换为编码丰富的临床数据。

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.