Construction of Relation Detection Framework Empowered by Topic Modeling

描述(由申请人提供)：大量EHR的供应增加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是一个巨大的挑战，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，这些语义可以作为EHR之间不同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，将这三种关系结合起来构建更好的患者队列识别系统的研究还不多。因此，PI建议为EHR开发一个具有主题建模的关系检测框架，以更准确地识别患者队列。在指导阶段，PI将在我的指导团队的指导下实施关系检测框架，并将以开源方式提供这些框架，以便它们可以适应其他机构的部署(目标1-K99)。在独立阶段，PI将研究促进类似系统的快速开发、部署和跨机构可移植性的方法。具体地说，PI将分别为EHR和Medline发现的数据语义开发ICD-9、RxNorm和Mesh本体的混合设计，并调查与医学本体一致的数据语义分类(Aim 2-R00)。为了使其他研究人员能够重复使用已开发的方法和软件资源，更重要的是对数据语义进行更正或调整，将开发一个工具包，以支持建造和部署类似系统(AIM 3-R00)。独立阶段将与UTHealth和马里兰大学合作。PI的职业目标是成为临床信息学的科学领导者，专注于为高效患者检测EHR之间的关系 队列识别。该中心在计算语言学方面有深厚的背景，在医疗临床记录处理和分析方面有丰富的经验，并将得到刘红芳博士、Christopher Chote博士和Terry Therneau博士的指导，他们拥有互补的专业领域。导师阶段将在梅奥诊所罗切斯特进行，在那里PI将进行美国医疗系统、卫生系统工程、临床统计学和临床流行病学的课程，并将接受卫生信息学的导师培训，这是他需要继续加强的，因为他没有在博士教育中接受定期培训。在独立的R00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，努力为将信息学用于医疗保健做出独立的科学贡献。拟议工作的完成将使私人投资公司能够寻求进一步的资金，以试验已开发的系统的临床部署，衡量其临床影响，并将该方法扩展到其他临床领域和机构。这笔职业补助金将使PI能够确立自己作为独立调查员的地位，并为推动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。

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.