A framework to enhance radiology structured report by invoking NLP and DL: Models and Applications

项目摘要/摘要 在放射学实践中，及时和准确地制定报告与患者满意度密切相关， 医生的工作效率和报销。而美国放射学学院和放射学学会- 北美的TES已建议实施结构化报告，以促进清晰和一致 放射科医生和转诊临床医生之间的沟通，当前结构化报告的繁琐性质 系统使它们在用户中不受欢迎。最近，新兴的深度学习技术已经 在许多不同的自然语言处理任务(NLP)中得到了广泛而成功的应用。但是，当 在某个特定的fic领域中采用，如放射学，这些技术应该与广泛的 领域知识，以提高EFfi的准确性和准确性。因此，迫切需要利用 临床NLP和深度学习从根本上改变了放射学的报告。这一应用程序的长期目标是- 阳离子是为了改进放射学报告的形式、内容和质量，促进放射学报告的快速生成- OGY报告组织一致，文本标准化。总体目标是使用放射学--specific 本体、自然语言处理和计算机视觉技术，以及深度学习来构建放射学专业--fic知识 图形，然后将使用它来建立一个报告系统，该系统可以帮助放射科医生快速生成结构- 结构化和标准化的文本报告。这个项目的基本原理是通过集成新的临床NLP 技术、放射学专业知识图谱和新报告系统的开发，我们可以建立fi-c知识图谱和新的报告系统。 对放射学世界有更高层次的理解的托马斯塔系统。这个项目的特殊fic目标是 目的：(1)对放射学报告中的命名实体进行识别和规范化；(2)构建放射学专业知识体系(fic Knowledge 从自由文本和图像生成图表；以及(3)建立一个可以根据模板动态调整的报告系统 关于放射科医生以前的记录。这项申请中提出的研究是创新的，在申请人看来， 因为它在单个框架中结合了深度学习、NLP技术和领域知识来构建 全面和准确的知识图谱，将增强当前报告系统的工作flow。 这项拟议的研究意义重大，因为一种新的报告系统可以加快放射科医生的工作速度和速度 获取注释良好的数据集，以促进机器学习和数据科学。为了开发这样一种方法， 候选人彭一凡博士需要在临床NLP和放射学方面进行额外的培训和指导。在.期间 在K99阶段，彭博士将作为国家生物技术中心的研究员进行这项研究 信息。他将得到领先的文本挖掘和深度学习研究员陆志勇博士的指导，并与 由领先的放射学家和临床信息学研究员罗纳德·M·萨默斯博士指导。此应用程序 NIH独立之路奖(K99/R00)描述了一项职业发展计划，该计划将使Dr。 彭将实现成为临床NLP研究的独立研究员和领导者的职业目标。

PROJECT SUMMARY/ABSTRACT In radiology practices, timely and accurate formulation of reports is closely linked to patient satisfaction, physician productivity, and reimbursement. While the American College of Radiology and the Radiological Soci- ety of North America have recommended implementation of structured reporting to facilitate clear and consistent communication between radiologists and referring clinicians, cumbersome nature of current structured reporting systems made them unpopular amongst their users. Recently, the emerging techniques of deep learning have been widely and successfully applied in many different natural language processing tasks (NLP). However, when adopted in a certain specific domain, such as radiology, these techniques should be combined with extensive domain knowledge to improve efficiency and accuracy. There is, therefore, a critical need to take advantage of clinical NLP and deep learning to fundamentally change the radiology reporting. The long-term goal in this appli- cation is to improve the form, content, and quality of radiology reports and to facilitate rapid generation of radiol- ogy reports with consistent organization and standardized texts. The overall objective is to use radiology-specific ontology, NLP and computer vision techniques, and deep learning to construct a radiology-specific knowledge graph, which will then be used to build a reporting system that can assist radiologists to quickly generate struc- tured and standardized text reports. The rationale for this project is that through integration of new clinical NLP technologies, radiology-specific knowledge graphs, and development of new reporting system, we can build au- tomatous systems with a higher-level understanding of the radiological world. The specific aims of this project are to: (1) recognize and normalize named entities in radiology reports; (2) construct a radiology-specific knowledge graph from free-text and images; and (3) build a reporting system that can dynamically adjust templates based on radiologists' prior entries. The research proposed in this application is innovative, in the applicant's opinion, because it combines deep learning, NLP techniques, and domain knowledge in a single framework to construct comprehensive and accurate knowledge graphs that will enhance the workflow of the current reporting systems. The proposed research is significant because a novel reporting system can expedite radiologists' workflow and acquire well-annotated datasets that facilitate machine learning and data science. To develop such a method, the candidate, Dr. Yifan Peng, requires additional training and mentoring in clinical NLP and radiology. During the K99 phase, Dr. Peng will conduct this research as a research fellow at the National Center for Biotechnology Information. He will be mentored by Dr. Zhiyong Lu, a leading text mining and deep learning researcher, and co- mentored by Dr. Ronald M. Summers, a leading radiologist and clinical informatics researcher. This application for the NIH Pathway to Independence Award (K99/R00) describes a career development plan that will allow Dr. Peng to achieve the career goals of becoming an independent investigator and leader in the study of clinical NLP.