Construction of Relation Detection Framework Empowered by Topic Modeling

主题建模赋能的关系检测框架构建

• 批准号: 8804480
• 负责人: Ding Cheng Li
• 金额: $ 9.37万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-15 至 2017-06-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8804480
• 关键词: Address; Area; Back; Categories; Chronic; Chronic Disease; Clinic; Clinical; Clinical Engineering; Clinical Informatics; Clinical Research; Clinical Sciences; Collaborations; Complement; Computational Linguistics; Computer software; Cues; Data; Dependency; Detection; Development; Diabetes Mellitus; Disease; Doctor of Philosophy; Education; Educational workshop; Electronic Health Record; Engineering; Environment; Event; Foundations; Funding; Future; Goals; Grant; Health Care Costs; Health Services; Health system; Healthcare; Healthcare Systems; Hybrids; Hypertension; ICD-9; Informatics; Institution; International Classification of Disease Codes; Knowledge; Lead; Learning; Linguistics; Logic; Maryland; MeSH Thesaurus; Measures; Medical; Medical Informatics; Medical Research; Mental Depression; Mentors; Methodology; Methods; Modeling; Natural Language Processing; Nurses; Ontology; Patients; Performance; Phase; Physicians; Procedures; Process; Public Health; Public Health Informatics; Qualifier; Quality of Care; Records; Relative (related person); Research; Research Methodology; Research Personnel; Research Training; Resolution; Resources; Risk Factors; Running; Schools; Semantics; Structure; System; Terminology; Text; Time; Training; Universities; Work; base; care delivery; career; clinical epidemiology; clinical practice; cohort; data mining; design; empowered; evidence base; experience; improved; informatics training; knowledge base; news; open source; operation; portability; public health relevance; statistics

DESCRIPTION (provided by applicant): The availability of large volume of EHRs enhances the possibility for using them for health services, EBM and clinical research. However such functionality is currently limited to narrow areas of clinical practice, as relation detections between medical events among unstructured EHRs still pose a big challenge, consequently leading to the inaccuracy of patient cohort identification, especially for cross-institutional environment. Preliminary work by the PI has shown that topic modeling can discover data semantics, which can then be employed as significant cues for diverse relation detections among EHRs. Among them, co-referring, temporal relations and domain semantics are intertwined and positively correlated. Up to now, not much research is done to combine the three relations to build a better patient cohort identification system. Therefore, the PI proposes to develop a relation detection framework for EHRs empowered with topic modeling for more accurate patient cohort identification. In the mentored phase, the PI will implement the relation detection framework under the guidance of my mentor team and will make them available in open-source so that they can be adapted for deployment at other institutions (aim 1 - K99). In the independent phase, the PI will research methods to facilitate rapid development, deployment and cross-institutional portability of similar systems. Specifically, the PI will develo a hybrid design with ICD-9, RxNorm and MeSH ontologies for the data semantics discoveries from EHRs and MedLINE respectively and investigate categorization of data semantics aligning with medical ontologies (aim 2 - R00). To enable other researchers to reuse the developed methodologies and software resources and more importantly to make corrections or adjustments on data semantics, a toolkit will be developed that will support the construction and deployment of similar systems (aim 3 - R00). The independent phase will be in collaboration with both UTHealth and University of Maryland. The PI's career goal is to become a scientific leader in clinical informatics with a focus on relation detections among EHRs for efficient patient cohort identification. The PI has strong background in computational linguistics and rich experiences in medical clinical records processing and analyses, and will receive mentoring from Drs. Hongfang Liu, Christopher Chute, and Terry Therneau, who have complimentary areas of expertise. The mentored phase will be in Mayo Clinic Rochester where the PI will undertake courses in US healthcare system, health system engineering, clinical statistics and clinical epidemiology and will get mentored training in health informatics which is what he needs to continue to strengthen since he didn't get regular training in his PhD education. In the independent R00 phase, the PI will strive for making independent scientific contributions to the use of informatics for healthcare via the implementation of Aims 2 and 3 and via the independent collaborations internal and externally. Completion of the proposed work will enable the PI to seek further funding for piloting clinical deployment of the developed systems, measuring their clinical impact, and for scaling the approach to other clinical domains and institutions. The career grant will enable the PI to establish himself as an independent investigator and to make significant contributions towards advancing the construction of medical knowledge systems and clinical practices as well as clinical research.

描述（由申请人提供）：大量EHR的可用性增强了将其用于卫生服务、循证医学和临床研究的可能性。然而，这种功能目前仅限于临床实践的狭窄领域，因为非结构化EHR之间的医疗事件之间的关系检测仍然构成很大的挑战，从而导致患者队列识别的不准确性，特别是对于跨机构环境。PI的初步工作表明，主题建模可以发现数据语义，然后可以作为EHR之间的各种关系检测的重要线索。其中，共指、时间关系和领域语义相互交织、正相关。到目前为止，将这三种关系联合收割机结合起来构建一个更好的患者队列识别系统的研究还不多。因此，PI建议为EHR开发一个关系检测框架，并授权主题建模，以更准确地识别患者队列。在指导阶段，PI将在我的导师团队的指导下实施关系检测框架，并将其以开源形式提供，以便它们可以适用于其他机构（目标1 - K99）。在独立阶段，PI将研究促进类似系统的快速开发，部署和跨机构移植的方法。具体而言，PI将开发ICD-9、RxNorm和MeSH本体的混合设计，分别用于EHR和MedLINE的数据语义发现，并研究与医学本体一致的数据语义分类（目标2 -R 00）。为了使其他研究人员能够重新使用所开发的方法和软件资源，更重要的是，为了对数据语义进行更正或调整，将开发一个工具包，以支持类似系统的建设和部署（目标3 -R 00）。独立阶段将与UTHealth和马里兰州大学合作。PI的职业目标是成为临床信息学的科学领导者，专注于EHR之间的关系检测， 队列识别。PI在计算语言学方面有很强的背景，在医疗临床记录处理和分析方面有丰富的经验，并将接受刘洪芳博士，Christopher Chute和Terry Therneau的指导，他们拥有互补的专业领域。指导阶段将在马约诊所罗切斯特进行，PI将在那里学习美国医疗保健系统、卫生系统工程、临床统计学和临床流行病学课程，并将接受卫生信息学的指导培训，这是他需要继续加强的，因为他在博士教育中没有接受定期培训。在独立R 00阶段，PI将通过实施目标2和3以及通过内部和外部的独立合作，努力为医疗保健信息学的使用做出独立的科学贡献。完成拟议工作后，首席研究员便可寻求进一步拨款，以试验在临床上部署已开发的系统、衡量其临床影响，以及将该方法扩展至其他临床领域和机构。职业资助将使主要研究者成为独立的研究者，并为推进医学知识体系和临床实践以及临床研究的建设做出重大贡献。