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.

修改后的项目摘要/摘要部分 这项名为“癌症监测自然语言处理平台”的 UG3/UH3 提案是对 PAR 16-349 研究领域 1 的回应 (https://grants.nih.gov/grants/guide/pa-files/par-16- 349.html）专门解决自然语言处理（NLP）工具的开发，以促进从各种类型的非结构化电子医学中自动/无监督/最低限度监督地提取特定的离散癌症相关数据与癌症登记处活动相关的记录 (EMR) 通过多 PI 机制提交——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、来自美国的 Harry Hochheiser 教授。匹兹堡大学和肯塔基州癌症登记处/肯塔基大学的 Eric Durbin 教授当前的提案建立在 NCI 癌症研究信息学工具资助的先前工作的基础上。 （ITCR）计划（https://itcr.cancer.gov/）我们设想在迄今为止的工作基础上推进临床表型数据信息提取方法，以推动新的癌症监测模式，这将有利于基于医院的、在这种新模式中，监测计划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。最终集成到现有或新的可视化中尽管之前已经有一些关于从各种数据流中自动提取表型的工作，包括特定类型癌症的临床叙述或癌症监测的个体变量，但拟议的工作将是一项关于从各种数据流中自动提取表型的工作。这种通用性通过拟议项目的建模部分得到加强，该部分基于生物医学本体、术语、社区采用的约定和标准的最新进展。拥有三个 SEER 癌症登记处，为我们的决策过程提供了大规模癌症监测的坚实基础。