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: 10451798
• 负责人: Eric B. Durbin
• 金额: $ 64.97万
• 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/10451798
• 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.

项目总结/文摘