Construction of Relation Detection Framework Empowered by Topic Modeling

描述（由申请人提供）：大量电子病历的可用性提高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，因为在非结构化电子病历中检测医疗事件之间的关系仍然是一个很大的挑战，从而导致患者队列识别的不准确性，特别是在跨机构环境中。PI的初步工作表明，主题建模可以发现数据语义，然后可以将其作为电子病历之间各种关系检测的重要线索。其中，共指关系、时间关系和领域语义三者相互交织、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建议为电子病历开发一个具有主题建模功能的关系检测框架，以更准确地识别患者队列。在指导阶段，PI将在我的导师团队的指导下实现关系检测框架，并将它们以开源的形式提供，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快速开发、部署和跨机构可移植性的方法。具体来说，PI将分别为来自EHRs和MedLINE的数据语义发现开发一种与ICD-9、RxNorm和MeSH本体的混合设计，并研究与医学本体一致的数据语义分类（目标2 - R00）。为了使其他研究人员能够重用开发的方法和软件资源，更重要的是对数据语义进行修正或调整，将开发一个工具包，以支持类似系统的构建和部署（目标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，专注于电子病历之间的关系检测，以提高患者的效率

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.