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: 10441803
• 负责人: Eric B. Durbin
• 金额: $ 66.02万
• 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 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10441803
• 关键词: Academic Medical Centers; Address; Adopted; Area; Boston; Cancer Research Project; Clinical; Communities; Computerized Medical Record; Data; Decision Making; Development; Foundations; Funding; Grant; Hospitals; Individual; Information Retrieval; Kentucky; Malignant Neoplasms; Methods; Modeling; Natural Language Processing; Pediatric Hospitals; Phenotype; Process; Reporting; Research; Site; Solid; Speed; Supervision; Surveillance Program; Terminology; Universities; Visualization; Work; base; biomedical ontology; cancer type; clinical phenotype; data streams; informatics tool; interoperability; medical schools; neoplasm registry; phenotypic data; response; symposium; tool

Modified Project Summary/Abstract Section 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.

修改的项目摘要/摘要部分 该UG3/UH3提案标题为“癌症监测的自然语言处理平台”是针对Par 16-349的研究区域（https://grants.nih.gov/grants/ggrants/ggrants/par-files/par-files/par-16-349.html），专门针对自动/透明的工具（nlp）工具（nlp）的开发（nlp）的开发（nlp）与癌症注册活动有关的各种非结构化电子病历（EMR）的离散癌症相关数据。它是通过多PI的机制提交的 - 来自波士顿儿童医院/哈佛医学院的Guergana Savova教授，范德比尔特大学医学中心的Jeremy Warner博士，匹兹堡大学的Harry Hochheiser教授和肯塔基州肯塔基州肯塔基州肯塔基州癌症院子/肯塔基大学的Eric Durbin教授。当前的提案建立在由NCI癌症研究工具（ITCR）计划（https://itcr.cancer.gov/）资助的先前工作。我们设想迄今为止的工作，以推动方法提取信息提取临床表型数据所需的信息，以促进新的癌症监测范式，从而使基于医院的，基于州和国家癌症注册处受益。在这个新的范式中，监视计划将使用这些方法来提高癌症的速度，准确性和易感性。报告。拟议的Deepphe*CR平台可以在本地站点或中央部署，最终可以根据癌症监视社区的需要将其集成到现有或新的可视化和抽象工具中。尽管以前已经从各种数据流中进行自动表型提取一些工作，包括针对癌症监测的特定类型的癌症或个体变量的临床叙述，但拟议的工作将是迈向可推广信息提取的一步。这种通用性可扩展性和可扩展性。互操作性是通过拟议项目的建模部分加强的，该项目是基于生物医学本体论，术语，社区补习的公约和标准的最新进展。我们与三个SEER癌症注册机构的计划合作伙伴关系为我们的决策过程提供了大规模癌症监测的坚实基础。