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: 9980862
• 负责人: Eric B. Durbin
• 金额: $ 41.07万
• 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 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9980862
• 关键词: Academic Medical Centers; Address; Adopted; Advanced Development; Agreement; Architecture; Area; Automation; Automobile Driving; Biological Markers; Boston; Breslow Thickness; Cancer Research Project; Cancer Surveillance Research Program; Characteristics; Clinical; Collaborations; Colorectal; Communities; Computer software; Computerized Medical Record; Consultations; Contractor; Data; Data Element; Data Management Resources; Data Sources; Decision Making; Development; Diagnosis; Division of Cancer Control and Population Sciences; Documentation; Elements; Environment; Extensible Markup Language; Family; Feeds; Foundations; Funding; Gene Proteins; Genomics; Goals; Grant; Handedness; Histologic; Histology; Hospitals; Human; Individual; Information Retrieval; Kentucky; Link; Location; Louisiana; Lung; Malignant Neoplasms; Malignant neoplasm of ovary; Maps; Massachusetts; Medicine; Methods; Modeling; Natural Language Processing; Oncology; Ontology; Output; Ovarian; Pathology; Pathology Report; Pediatric Hospitals; Performance; Phase; Phenotype; Positioning Attribute; Process; Prostate; Provider; Radiation therapy; Radiology Specialty; Registries; Reporting; Research; Site; Solid; Source; Speed; Supervision; Surveillance Program; Terminology; Text; Tumor Debulking; Ulcer; Universities; Validation; Visualization; Work; base; biomedical ontology; calcification; cancer diagnosis; cancer invasiveness; cancer type; clinical encounter; clinical phenotype; cohort; data management; data streams; data visualization; demographics; informatics tool; innovation; interoperability; malignant breast neoplasm; medical schools; melanoma; neoplasm registry; novel; open source; patient health information; phenotypic data; point of care; response; satisfaction; software development; success; symposium; tool; transmission process; treatment center; tumor

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.

项目概要/摘要 这项名为“癌症监测自然语言处理平台”的 UG3/UH3 提案就是对此的回应 具体到 PAR 16-349 的研究领域 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，以促进 自动/无监督/最低限度监督地从各种不同的数据中提取特定的离散癌症相关数据 与癌症登记处活动相关的非结构化电子病历 (EMR) 类型。 通过多 PI 机制提交 – 来自波士顿儿童医院/哈佛大学的 Guergana Savova 教授 医学院，范德堡大学医学中心 Jeremy Warner 博士，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 (ITCR) 资助的先前工作的基础上 我们设想以我们迄今为止的工作为基础，推进治疗癌症的方法。 临床表型数据的信息提取需要推动新的癌症监测范式 在这种新的范式中，监测将有利于医院、州和国家癌症登记处。 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，并且最终可以 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 尽管之前已经有一些关于从各种自动表型提取方面的工作。 数据流，包括特定类型癌症的临床叙述或癌症的个体变量 监督，拟议的工作将是迈向普遍信息提取的一步。 通用性使可扩展性和可伸缩性通过建模部分得到加强。 拟议的项目以生物医学本体论、术语、 我们计划与三个 SEER 癌症登记处建立合作伙伴关系。 为我们的决策过程提供大规模癌症监测的坚实基础。