Natural Language Processing Platform for Cancer Surveillance

项目概要/摘要 UG 3/UH 3的这份名为“癌症监测的自然语言处理平台”的提案是为了回应 PAR 16-349研究领域1（https：//grants.nih.gov/grants/guide/pa-files/par-16-349.html） 解决自然语言处理（NLP）工具的开发问题， 自动/无监督/最低限度监督的特定离散癌症相关数据的提取 与癌症登记活动相关的非结构化电子病历（EMR）类型。是 通过多PI机制提交-来自波士顿儿童医院/哈佛的Guergana Savova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 匹兹堡大学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）资助的先前工作的基础上 程序（https：itcr.cancer.gov/）。我们设想在我们迄今为止的工作基础上， 临床表型数据的信息提取需要推动一种新的癌症监测模式， 将使医院、州和国家癌症登记处受益。在这种新的模式下， 计划将使用这些方法来提高癌症报告的速度，准确性和易用性。的 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 根据癌症监测的需要集成到现有或新的可视化和抽象工具中 社区虽然已经有一些以前的工作自动表型提取从各种 数据流，包括特定类型癌症的临床叙述或癌症的个体变量 为了加强监测，拟议的工作将是朝着普遍提取信息迈出的一步。这 通用性实现了可扩展性和可缩放性。的建模部分增强了互操作性， 拟议的项目是在生物医学本体，术语， 共同体通过的公约和标准。我们计划与三个SEER癌症登记处合作 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.