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

通过调用 NLP 和 DL 来增强放射学结构化报告的框架：模型和应用

• 批准号: 10197509
• 负责人: Yifan Peng
• 金额: $ 23.65万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10197509
• 关键词: Address; Adopted; American College of Radiology; Award; Biotechnology; Caring; Client satisfaction; Clinical; Clinical Data; Clinical Informatics; Communication; Complex; Computer Vision Systems; Data Science; Data Set; Development; Development Plans; Formulation; Generations; Goals; Health Services; Hospitals; Hybrids; Image; Knowledge; Learning; Link; Machine Learning; Medical; Mentors; Methods; Mission; Modeling; Mus; Names; Natural Language Processing; Nature; Nomenclature; North America; Ontology; Outcome; Pathway interactions; Patients; Phase; Physicians; Picture Archiving and Communication System; Process; Productivity; Public Health; Radiology Specialty; Reporting; Research; Research Personnel; Resort; Societies; Standardization; Structure; System; Systems Development; Techniques; Technology; Terminology; Text; Time; Training; United States National Institutes of Health; Voice; Writing; base; career; career development; convolutional neural network; deep learning; deep neural network; impression; improved; innovation; knowledge graph; lexical; long short term memory; neural network; neural network architecture; novel; radiologist; repository; response; syntax; text searching

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.

项目摘要/摘要 在放射学实践中，及时准确地制定报告与患者满意度密切相关， 医生的生产力和报销。虽然美国放射学会和放射学会- 北美一些国家建议实施结构化报告， 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。然而当 在某些特定领域（如放射学）采用这些技术时，应结合广泛的 领域知识，以提高效率和准确性。因此，迫切需要利用 临床NLP和深度学习从根本上改变放射学报告。在这个应用程序中的长期目标- 目的是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报告具有一致的组织结构和标准化的文本。总体目标是使用放射学特异性 本体论、NLP和计算机视觉技术，以及深度学习，以构建放射学特定知识 图，然后将用于建立一个报告系统，可以帮助放射科医生快速生成结构， 规范和标准化的文本报告。该项目的基本原理是，通过整合新的临床NLP 技术，放射学专业知识图，以及新报告系统的开发，我们可以建立Au- 对放射性世界有更高层次的了解。该项目的具体目标是 （1）识别和规范化放射学报告中的命名实体;（2）构建放射学特定知识 图从自由文本和图像;和（3）建立一个报告系统，可以动态调整模板的基础上 放射科医生之前的记录申请人认为，本申请中提出的研究具有创新性， 因为它将深度学习、NLP技术和领域知识结合在一个框架中， 全面和准确的知识图表，将加强目前报告制度的工作流程。 这项拟议中的研究意义重大，因为一种新的报告系统可以加快放射科医生的工作速度， 获取注释良好的数据集，以促进机器学习和数据科学。为了开发这种方法， 候选人，Yifan Peng博士，需要在临床NLP和放射学方面的额外培训和指导。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.他将接受领先的文本挖掘和深度学习研究人员Zhiyong Lu博士的指导，并与 由罗纳德M.他是一位顶尖的放射学家和临床信息学研究员。本申请 美国国立卫生研究院独立之路奖（K99/R 00）描述了一个职业发展计划，将允许博士。 Peng的职业目标是成为临床NLP研究的独立研究者和领导者。