Natural Language Processing Platform for Cancer Surveillance

用于癌症监测的自然语言处理平台

• 批准号: 9980862
• 负责人: Eric B. Durbin
• 金额: $ 41.07万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-19 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9980862
• 关键词: Academic Medical Centers; Address; Adopted; Advanced Development; Agreement; Architecture; Area; Automation; Automobile Driving; Biological Markers; Boston; Breslow Thickness; Cancer Research Project; Cancer Surveillance Research Program; Characteristics; Clinical; Collaborations; Colorectal; Communities; Computer software; Computerized Medical Record; Consultations; Contractor; Data; Data Element; Data Management Resources; Data Sources; Decision Making; Development; Diagnosis; Division of Cancer Control and Population Sciences; Documentation; Elements; Environment; Extensible Markup Language; Family; Feeds; Foundations; Funding; Gene Proteins; Genomics; Goals; Grant; Handedness; Histologic; Histology; Hospitals; Human; Individual; Information Retrieval; Kentucky; Link; Location; Louisiana; Lung; Malignant Neoplasms; Malignant neoplasm of ovary; Maps; Massachusetts; Medicine; Methods; Modeling; Natural Language Processing; Oncology; Ontology; Output; Ovarian; Pathology; Pathology Report; Pediatric Hospitals; Performance; Phase; Phenotype; Positioning Attribute; Process; Prostate; Provider; Radiation therapy; Radiology Specialty; Registries; Reporting; Research; Site; Solid; Source; Speed; Supervision; Surveillance Program; Terminology; Text; Tumor Debulking; Ulcer; Universities; Validation; Visualization; Work; base; biomedical ontology; calcification; cancer diagnosis; cancer invasiveness; cancer type; clinical encounter; clinical phenotype; cohort; data management; data streams; data visualization; demographics; informatics tool; innovation; interoperability; malignant breast neoplasm; medical schools; melanoma; neoplasm registry; novel; open source; patient health information; phenotypic data; point of care; response; satisfaction; software development; success; symposium; tool; transmission process; treatment center; tumor

PROJECT SUMMARY/ABSTRACT This UG3/UH3 proposal titled “Natural Language Processing Platform for Cancer Surveillance” is in response to Research Area 1 of PAR 16-349 (https://grants.nih.gov/grants/guide/pa-files/par-16-349.html) specifically addressing the development of natural language processing (NLP) tools to facilitate automatic/unsupervised/minimally supervised extraction of specific discrete cancer-related data from various types of unstructured electronic medical records (EMRs) related to the activities of cancer registries. It is submitted through a multi-PI mechanism – Prof. Guergana Savova from Boston Children’s Hospital/Harvard Medical School, Dr. Jeremy Warner from Vanderbilt University Medical Center, Prof. Harry Hochheiser from the University of Pittsburgh, and Prof. Eric Durbin from the Kentucky Cancer Registry/University of Kentucky. The current proposal builds on prior work funded by the NCI Informatics Tools for Cancer Research (ITCR) program (https://itcr.cancer.gov/ ). We envision building on our work to date to advance methods for information extraction of clinical phenotyping data needed to fuel a new cancer surveillance paradigm that would benefit hospital-based, state-based, and national cancer registries. In this new paradigm, surveillance programs would use the methods to enhance the speed, accuracy, and ease of cancer reporting. The proposed DeepPhe*CR platform could be deployed at local sites or centrally, and could eventually be integrated into existing or new visualization and abstraction tools as needed by the cancer surveillance community. Although there has been some previous work on automatic phenotype extraction from the various streams of data including the clinical narrative for specific types of cancer or individual variables for cancer surveillance, the proposed work will be a step towards a generalizable information extraction. This generalizability enables extensibility and scalability. Interoperability is reinforced through the modeling part of the proposed project which is grounded in most recent advances in biomedical ontologies, terminologies, community-adopted conventions and standards. Our planned partnership with three SEER cancer registries provides our decision-making processes with a solid foundation in large-scale cancer surveillance.

项目摘要/摘要 该ug3/uh3提案标题为“癌症监测的自然语言处理平台” 到16-349 PAR 1的研究区（https://grants.nih.gov/grants/guide/pa-files/par-16-349.html） 解决自然语言处理（NLP）工具的开发以促进 自动/无监督/最小监督的特定离散癌症相关数据 与癌症注册活动有关的非结构化电子病历（EMR）的类型。这是 通过多PI机制提交 - 来自波士顿儿童医院/哈佛大学的Guergana Savova教授 医学院，范德比尔特大学医学中心的Jeremy Warner博士，哈里·霍希塞尔教授 匹兹堡大学和肯塔基州肯塔基大学/肯塔基大学的Eric Durbin教授。 当前的提案是基于由NCI癌症研究工具（ITCR）资助的先前工作。 程序（https://itcr.cancer.gov/）。我们设想迄今为止的工作以推动 临床表型数据的信息提取为加油的新癌症监测范式所需的信息提取 将受益于基于医院的，州和国家癌症登记处。在这个新的范式中，监视 程序将使用这些方法来提高癌症报告的速度，准确性和易度性。 拟议的Deepphe*Cr平台可以部署在本地地点或中央，最终可能是 根据癌症监视的需要集成到现有或新的可视化和抽象工具中 社区。尽管以前有一些关于从各种自动表型提取的工作 数据流，包括特定类型的癌症或癌症各个变量的临床叙述 监视，拟议的工作将是迈向可推广信息提取的一步。 概括性可扩展性和可伸缩性。互操作性通过建模部分增强 拟议的项目是基于生物医学本体论，术语，术语的最新进展的基础 社区补习的惯例和标准。我们与三个先知癌症注册机构的计划合作 在大规模的癌症监测中为我们的决策过程提供了坚实的基础。