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.

项目概要/摘要 在放射实践中，及时、准确地制定报告与患者满意度密切相关， 而美国放射学会和放射学会。 北美建议实施结构化报告，以促进清晰和一致 放射科医生之间的沟通以及当前结构化报告的忠实性、繁琐性 最近，新兴的深度学习技术使得它们在用户中不受欢迎。 已被广泛且成功地应用于许多不同的自然语言处理任务（NLP）中。 在某些特定领域（例如放射学）采用时，这些技术应与广泛的相结合 因此，迫切需要利用领域知识来提高效率和准确性。 临床 NLP 和深度学习从根本上改变放射学报告是该应用程序的长期目标。 其目的是改进放射学报告的形式、内容和质量，并促进放射学报告的快速生成。 总体目标是使用放射学特定的内容。 本体论、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特定知识 图表，然后将用于构建报告系统，可以帮助放射科医生快速生成结构 该项目的基本原理是通过整合新的临床 NLP。 技术、放射学特定知识图谱以及新报告系统的开发，我们可以构建 au- 对放射学世界有更高层次理解的番茄系统 该项目的具体目标是 目的：(1) 识别并规范放射学报告中的命名实体；(2) 构建放射学特定知识； 来自自由文本和图像的图表；（3）构建一个可以根据模板动态调整的报告系统 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来构建 全面而准确的知识图谱将增强当前报告系统的工作流程。 拟议的研究意义重大，因为新颖的报告系统可以加快放射科医生的工作流程并 获取有利于机器学习和数据科学的注释良好的数据集来开发这样的方法， 候选人彭一凡博士在临床 NLP 和放射学方面需要额外的培训和指导。 K99阶段，彭博士将作为国家生物技术中心研究员进行这项研究 他将受到领先的文本挖掘和深度学习研究员Zhiyong Lu博士的指导，并共同参与。 由领先的放射科医生和临床信息学研究员 Ronald M. Summers 博士指导。 NIH 独立之路奖 (K99/R00) 描述了一项职业发展计划，该计划将使博士能够 Peng 实现成为临床 NLP 研究的独立研究者和领导者的职业目标。