UH3CA243120
Cooperative Agreement
Overview
Grant Description
Natural Language Processing Platform for Cancer Surveillance - 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, it addresses 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 the 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.
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, it addresses 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 the 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.
Awardee
Funding Goals
TO IDENTIFY CANCER RISKS AND RISK REDUCTION STRATEGIES, TO IDENTIFY FACTORS THAT CAUSE CANCER IN HUMANS, AND TO DISCOVER AND DEVELOP MECHANISMS FOR CANCER PREVENTION AND PREVENTIVE INTERVENTIONS IN HUMANS. RESEARCH PROGRAMS INCLUDE: (1) CHEMICAL, PHYSICAL AND MOLECULAR CARCINOGENESIS, (2) SCREENING, EARLY DETECTION AND RISK ASSESSMENT, INCLUDING BIOMARKER DISCOVERY, DEVELOPMENT AND VALIDATION, (3) EPIDEMIOLOGY, (4) NUTRITION AND BIOACTIVE FOOD COMPONENTS, (5) IMMUNOLOGY AND VACCINES, (6) FIELD STUDIES AND STATISTICS, (7) CANCER CHEMOPREVENTION AND INTERCEPTION, (8) PRE-CLINICAL AND CLINICAL AGENT DEVELOPMENT, (9) ORGAN SITE STUDIES AND CLINICAL TRIALS, (10) HEALTH-RELATED QUALITY OF LIFE AND PATIENT-CENTERED OUTCOMES, AND (11) SUPPORTIVE CARE AND MANAGEMENT OF SYMPTOMS AND TOXICITIES. SMALL BUSINESS INNOVATION RESEARCH (SBIR) PROGRAM: TO EXPAND AND IMPROVE THE SBIR PROGRAM, TO STIMULATE TECHNICAL INNOVATION, TO INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT FUNDING, TO INCREASE SMALL BUSINESS PARTICIPATION IN FEDERAL RESEARCH AND DEVELOPMENT, AND TO FOSTER AND ENCOURAGE PARTICIPATION IN INNOVATION AND ENTREPRENEURSHIP BY WOMEN AND SOCIALLY/ECONOMICALLY DISADVANTAGED PERSONS. SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAM: TO STIMULATE AND FOSTER SCIENTIFIC AND TECHNOLOGICAL INNOVATION THROUGH COOPERATIVE RESEARCH AND DEVELOPMENT CARRIED OUT BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO FOSTER TECHNOLOGY TRANSFER THROUGH COOPERATIVE RESEARCH AND DEVELOPMENT BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT FUNDING, AND FOSTER PARTICIPATION IN INNOVATION AND ENTREPRENEURSHIP BY WOMEN AND SOCIALLY/ECONOMICALLY DISADVANTAGED PERSONS.
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Boston,
Massachusetts
021155724
United States
Geographic Scope
Single Zip Code
Related Opportunity
Analysis Notes
Amendment Since initial award the total obligations have increased 217% from $660,155 to $2,093,016.
Children's Hospital Corporation was awarded
Natural Language Processing Platform for Cancer Surveillance
Cooperative Agreement UH3CA243120
worth $2,093,016
from National Cancer Institute in July 2019 with work to be completed primarily in Boston Massachusetts United States.
The grant
has a duration of 5 years and
was awarded through assistance program 93.393 Cancer Cause and Prevention Research.
The Cooperative Agreement was awarded through grant opportunity New Informatics Tools and Methods to Enhance US Cancer Surveillance and Research (UG3/UH3) .
Status
(Complete)
Last Modified 11/20/24
Period of Performance
7/19/19
Start Date
6/30/24
End Date
Funding Split
$2.1M
Federal Obligation
$0.0
Non-Federal Obligation
$2.1M
Total Obligated
Activity Timeline
Subgrant Awards
Disclosed subgrants for UH3CA243120
Transaction History
Modifications to UH3CA243120
Additional Detail
Award ID FAIN
UH3CA243120
SAI Number
UH3CA243120-2160987776
Award ID URI
SAI UNAVAILABLE
Awardee Classifications
Nonprofit With 501(c)(3) IRS Status (Other Than An Institution Of Higher Education)
Awarding Office
75NC00 NIH NATIONAL CANCER INSTITUTE
Funding Office
75NC00 NIH NATIONAL CANCER INSTITUTE
Awardee UEI
Z1L9F1MM1RY3
Awardee CAGE
2H173
Performance District
MA-07
Senators
Edward Markey
Elizabeth Warren
Elizabeth Warren
Budget Funding
| Federal Account | Budget Subfunction | Object Class | Total | Percentage |
|---|---|---|---|---|
| National Cancer Institute, National Institutes of Health, Health and Human Services (075-0849) | Health research and training | Grants, subsidies, and contributions (41.0) | $1,440,667 | 100% |
Modified: 11/20/24