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: 10224953
• 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/10224953
• 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，准确的报告公式与患者满意度密切相关， 医师生产率和报销。而美国放射学和放射学学院 北美的Ety建议实施结构化报告，以促进清晰，一致的 放射科医生与参考临床医生之间的沟通，当前结构化报告的繁琐性质 系统使他们在用户中不受欢迎。最近，深度学习的新兴技术已经 被广泛应用于许多不同的自然语言处理任务（NLP）。但是，什么时候 在某个特定领域（例如放射学）中采用的这些技术应与广泛的 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。这项应用的长期目标 阳离子是为了改善放射学报告的形式，内容和质量，并促进快速产生放射性 OGY报告具有一致的组织和标准化文本。总体目的是使用放射学特异性 本体论，NLP和计算机视觉技术以及深入学习以构建放射学知识 图，然后将用于构建一个报告系统，该系统可以帮助放射科医生快速生成结构 - 特征和标准化的文本报告。该项目的理由是通过整合新的临床NLP 技术，放射学特定知识图和新报告系统的开发，我们可以建立 对放射学世界有更高层次了解的静态系统。该项目的特定目的是 至：（1）放射学报告中公认和标准化的实体； （2）构建放射学的知识 自由文本和图像的图； （3）建立一个可以动态调整基于模板的报告系统 关于放射学家的先前条目。该应用程序认为，本申请中提出的研究具有创新性 因为它结合了深度学习，NLP技术和领域知识，以构建 全面，准确的知识图将增强当前报告系统的工作流程。 拟议的研究非常重要，因为新型的报告系统可以加快放射科医生的工作流程，并且 获取良好的数据集，以促进机器学习和数据科学。为了开发这样的方法， 候选人Yifan Peng博士需要在临床NLP和放射学上进行额外的培训和心理。期间 K99阶段，Peng博士将在国家生物技术中心作为研究研究员进行这项研究 信息。他将由领先的文本挖掘和深度学习研究人员的卢博士召集他，并共同 由放射科医生和临床信息研究员Ronald M. Summers博士指导。此应用程序 对于NIH独立奖（K99/R00），描述了一项职业发展计划，该计划将允许博士 彭（Peng）实现了成为临床NLP研究的独立研究者和领导者的职业目标。