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.

修改项目摘要/摘要部分 UG 3/UH 3提案标题为“用于癌症监测的自然语言处理平台”，是对PAR 16-349研究领域1（https：//grants.nih.gov/grants/guide/pa-files/par-16-349.html）的响应，该研究领域1专门针对自然语言处理（NLP）工具的开发，以促进从与癌症登记活动相关的各种类型的非结构化电子病历（EMR）中自动/无监督/最低监督提取特定离散癌症相关数据。通过多PI机制提交-来自波士顿儿童医院/哈佛医学院的Guergana Savova教授、来自范德比尔特大学医学中心的Jeremy Warner博士、来自匹兹堡大学的Harry Hochheiser教授和来自肯塔基州癌症登记处/肯塔基州大学的Eric Durbin教授。目前的提案建立在NCI癌症研究信息学工具（ITCR）计划（https：//www.example.com）资助的先前工作的基础上。itcr.cancer.gov/我们设想在我们迄今为止的工作的基础上，推进临床表型数据的信息提取方法，以推动新的癌症监测范式，这将有利于医院，州和国家癌症登记。在这种新的模式中，监测项目将使用这些方法来提高癌症报告的速度、准确性和便利性。拟议的DeepPhe*CR平台可以部署在本地站点或集中部署，最终可以根据癌症监测社区的需要集成到现有或新的可视化和抽象工具中。虽然已经有一些以前的工作自动表型提取的各种数据流，包括特定类型的癌症或癌症监测的个体变量的临床叙述，拟议的工作将是一个可推广的信息提取的一步。这种通用性支持可扩展性和可伸缩性。通过拟议项目的建模部分加强了互操作性，该项目基于生物医学本体、术语、社区采用的惯例和标准的最新进展。我们计划与三个SEER癌症登记处建立伙伴关系，为我们的决策过程提供了大规模癌症监测的坚实基础。