Increasing Clinical Trial Enrollment: A Semi-Automated Patient Centered Approach

增加临床试验注册人数：以患者为中心的半自动化方法

• 批准号: 8331381
• 负责人: Imre Solti
• 金额: $ 23.89万
• 依托单位: CINCINNATI CHILDRENS HOSP MED CTR
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-10-02 至 2013-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8331381
• 关键词: Adult; Age; Algorithms; Applications Grants; Biomedical Research; Cancer Patient; Characteristics; Clinical; Clinical Research; Clinical Trials; Clinical trial protocol document; Code; Commit; Complement; Computer software; Computerized Medical Record; Decision Making; Development; Elderly; Elements; Eligibility Determination; Enrollment; Ensure; Equation; Evaluation; Faculty; Foundations; Future; Goals; Gold; Grant; In Vitro; Informatics; Java; Knowledge; Laboratories; Linguistics; Malignant Neoplasms; Maps; Medical Records; Medicine; Methods; Minority; Modification; Natural Language Processing; Newly Diagnosed; Observational Study; Patients; Performance; Pharmaceutical Preparations; Physicians; Process; Protocols documentation; Public Health Informatics; Publishing; Recommendation; Records; Recruitment Activity; Research; Research Design; Role; Screening for cancer; Screening procedure; Specialist; Surgeon; Text; Training; Unified Medical Language System; United States National Library of Medicine; Work; abstracting; base; cancer care; empowered; ethnic minority population; information organization; knowledge base; novel; older patient; patient oriented; programs; prospective; research study; software development; success; tool

Abstract The long-term objective of this research is to increase the clinical trial enrollment of US patients via a semi- automated, Natural Language Processing (NLP) based, interactive and patient-centered informatics application. The study design is prospective observational study. Scope is limited to cancer patients. There are three specific aims for this project. The first aim is to identify concepts that overlap between the electronic medical record's (EMR) clinical notes and the free text of clinical trial announcements. The PI will use the concepts to develop mapping frames that connect concepts in the text of trial announcements to those found in clinical notes in the medical record. When he has the mapping frames he will build the NLP module for the application. In the software development work he will utilize as many publicly available software components as possible. He will experiment with UIMA, GATE, MetaMap, Stanford Parser, NegEx algorithm and others. The PI will develop the tool around the National Library of Medicine's Unified Medical Language System knowledgebase. He will use Java for programming. The second aim is to create an algorithm that automatically generates questions to request information directly from the patient if the information is not available or accessible in the records. The third aim is to evaluate the in-vitro, laboratory performance of the application. For performance evaluation purposes the PI will recruit cancer care specialists to generate the gold standard lists of eligible clinical trials for study patients. He will publicly release the developed code at the end of the grant period. This K99/R00 project will serve the foundation for future R01 grant applications. The PI is fully committed to become faculty in the Clinical Research Informatics domain with a specialization in biomedical NLP. The support of the K99/R00 grant will enable him to acquire substantial formal training in Computational Linguistics while contributing to the body of knowledge of the Clinical Research Informatics field. The five-year grant support will ensure success in his endeavor. The proposed work is highly significant because the dismal clinical trial accrual rates (2-4 % nationally) hampers timely development of new drugs. In addition, studies show that physicians have statistically significant bias against elderly and minority patients to invite participation in clinical trials. The proposed project is synergistic with physician-centered efforts but the goal is to provide individualized, EMR based clinical trial recommendations directly to the patients. The results of this research will empower the patients and elevate their role in the decision making process.

摘要 本研究的长期目标是通过半定量研究增加美国患者的临床试验入组， 自动化，基于自然语言处理（NLP），交互式和以患者为中心的信息学 应用程序.研究设计为前瞻性观察性研究。范围仅限于癌症患者。有 该项目的三个具体目标。第一个目标是确定电子商务与 病历（EMR）临床记录和临床试验公告的自由文本。PI将使用 概念开发映射框架，将审判公告文本中的概念与 病历中的临床记录。当他有了映射框架后，他将为 应用程序.在软件开发工作中，他将利用尽可能多的公开可用的软件组件 越好.他将尝试UIMA、GATE、MetaMap、斯坦福大学Parser、NegEx算法等。 PI将围绕国家医学图书馆的统一医学语言系统开发工具 知识库他将使用Java编程。第二个目标是创建一个算法， 如果信息不可用，则生成问题以直接向患者请求信息，或者 在记录中可访问。第三个目的是评价应用程序的体外实验室性能。 出于绩效评估的目的，PI将招募癌症护理专家，以制定黄金标准 研究患者的合格临床试验列表。他将在年底公开发布开发的代码， 补助期。这个K99/R 00项目将为未来的R 01赠款申请奠定基础。PI完全 致力于成为临床研究信息学领域的教师，专门从事生物医学 NLP。K99/R 00赠款的支持将使他能够获得大量的正式培训， 语言学，同时有助于临床研究信息学领域的知识体系。五年 政府的支持将确保他的奋进取得成功。拟议的工作非常重要，因为令人沮丧的 临床试验累积率（全国为2- 4%）阻碍了新药的及时开发。另外研究 显示医生对老年人和少数民族患者有统计学上的显着偏见， 参与临床试验。拟议的项目是协同与医生为中心的努力，但目标是 直接向患者提供个性化的、基于EMR的临床试验建议。的结果 研究将赋予病人权力，并提高他们在决策过程中的作用。

项目成果

Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department.

• DOI: 10.1136/amiajnl-2014-002887
• 发表时间: 2015-01
• 期刊: Journal of the American Medical Informatics Association : JAMIA
• 作者: Ni Y;Kennebeck S;Dexheimer JW;McAneney CM;Tang H;Lingren T;Li Q;Zhai H;Solti I
• 通讯作者: Solti I

Developing and evaluating an automated appendicitis risk stratification algorithm for pediatric patients in the emergency department.

• DOI: 10.1136/amiajnl-2013-001962
• 发表时间: 2013-12
• 期刊: Journal of the American Medical Informatics Association : JAMIA
• 作者: Deleger L;Brodzinski H;Zhai H;Li Q;Lingren T;Kirkendall ES;Alessandrini E;Solti I
• 通讯作者: Solti I

Increasing the efficiency of trial-patient matching: automated clinical trial eligibility pre-screening for pediatric oncology patients.

• DOI: 10.1186/s12911-015-0149-3
• 发表时间: 2015-04-14
• 期刊: BMC medical informatics and decision making
• 影响因子: 3.5
• 作者: Ni Y;Wright J;Perentesis J;Lingren T;Deleger L;Kaiser M;Kohane I;Solti I
• 通讯作者: Solti I

Building gold standard corpora for medical natural language processing tasks.

为医学自然语言处理任务构建黄金标准语料库。

• 发表时间: 2012
• 期刊: AMIA ... Annual Symposium proceedings. AMIA Symposium
• 作者: Deleger,Louise;Li,Qi;Lingren,Todd;Kaiser,Megan;Molnar,Katalin;Stoutenborough,Laura;Kouril,Michal;Marsolo,Keith;Solti,Imre
• 通讯作者: Solti,Imre

Using Amazon's Mechanical Turk for Annotating Medical Named Entities.

使用 Amazon Mechanical Turk 注释医学命名实体。

• 发表时间: 2010
• 期刊: AMIA ... Annual Symposium proceedings. AMIA Symposium
• 作者: Yetisgen-Yildiz,Meliha;Solti,Imre;Xia,Fei
• 通讯作者: Xia,Fei