Natural Language Processing Platform for Cancer Surveillance

项目摘要/摘要 UG3/UH3的这份题为《用于癌症监测的自然语言处理平台》的提案是为了回应 专门研究PAR 16-349(https://grants.nih.gov/grants/guide/pa-files/par-16-349.html)的第1区 解决自然语言处理(NLP)工具的开发问题，以促进 自动/无监督/最小监督从不同类型的数据中提取特定离散癌症相关数据 与癌症登记处活动有关的非结构化电子病历(EMR)类型。它是 通过多PI机制提交--来自波士顿儿童医院/哈佛大学的Guergana Svova教授 医学院，来自范德比尔特大学医学中心的Jeremy Warner博士，来自 来自匹兹堡大学和肯塔基州癌症登记处/肯塔基大学的埃里克·德宾教授。 目前的提案建立在由NCI癌症研究信息学工具(ITCR)资助的先前工作的基础上。 程序(https://itcr.cancer.gov/)。我们设想以我们迄今的工作为基础，为 临床表型数据的信息提取需要为新的癌症监测范例提供燃料， 将使基于医院、基于州和国家的癌症登记机构受益。在这个新的范式中，监视 项目将使用这些方法来提高癌症报告的速度、准确性和简便性。这个 拟议的DeepPhe*CR平台可以部署在本地站点或集中部署，并最终可能 根据癌症监测的需要集成到现有的或新的可视化和抽象工具中 社区。尽管以前有一些工作是关于从各种不同的 数据流，包括特定癌症类型的临床叙述或癌症的个体变量 在监测方面，拟议的工作将是朝着可推广的信息提取迈出的一步。这 通用性实现了可扩展性和可伸缩性。的建模部分增强了互操作性 拟议的项目基于生物医学本体论、术语、 社区通过的公约和标准。我们计划与三个SEER癌症登记处建立合作关系 为我们的决策过程提供了大规模癌症监测的坚实基础。

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.