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PRECISE - a PErsonalized Risk Score for gastrIc CancEr

PRECISE - 胃癌的个性化风险评分

基本信息

批准号：
10550247
负责人：
Robert Jeffrey Huang
金额：
$ 18.82万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-03-01 至 2026-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10550247
关键词：
Achievement Address Age Alaska Native American Indians Asian population Attention Black Populations Board Certification Calibration Characteristics Clinical Clinical Data Clinical Informatics Collection Communication Data Data Analytics Data Science Data Set Data Sources Development Development Plans Diagnosis Discrimination Disease Electronic Health Record Ensure Environment Epidemiologist Epidemiology Equity Ethnic Origin Ethnic Population Excision Face Funding Gastroenterologist General Population Goals Grant Health Disparities Research Health Services Research Healthcare Helicobacter pylori Hispanic Populations Immigrant Individual Inequity Informatics Information Retrieval Inpatients Institution Laboratories Lasso Leadership Machine Learning Malignant Neoplasms Malignant neoplasm of gastrointestinal tract Master of Science Measures Medicare Mentors Mentorship Methodology Modeling Morbidity - disease rate National Cancer Institute Natural Language Processing Nature Neighborhoods Not Hispanic or Latino Observational Study Outcome Outpatients Performance Pharmacy facility Phenotype Poverty Precision Health Prevention Probability Prognosis Race Reporting Research Research Design Research Personnel Risk Risk Factors Screening for Gastric Cancer Smoking Subgroup System Techniques Testing Time Training Unemployment United States Universities Validation Work advanced analytics attenuation cancer diagnosis cancer health disparity cancer risk career development clinically actionable cohort data mining data streams deep learning disparity elimination disparity reduction experience gastric cancer prevention health care disparity health equity high risk high risk population improved improved outcome learning algorithm learning strategy machine learning algorithm machine learning model malignant stomach neoplasm mortality multi-ethnic neural network novel outcome prediction personalized predictions personalized risk prediction programs racial diversity random forest risk prediction risk prediction model sex skills success supervised learning support vector machine tool unstructured data years of life lost

项目摘要

The National Cancer Institute has called for eliminating disparities in cancer morbidity and mortality through the use of Data Science. Gastric cancer remains one of the most unequally distributed cancers in the United States, with high burden among certain ethnic, racial, and immigrant groups. Identification of individuals at greatest risk for gastric cancer may allow for targeted risk attenuation programs, and improve health equity. Candidate and Career Development Plan: I am a board-certified Gastroenterologist and Master’s degree-trained epidemiologist at Stanford University who seeks to use data science to reduce disparities in cancer outcomes. Based on my training and experience, I have content expertise in gastrointestinal cancer diagnosis, and methodologic expertise in epidemiologic principles and observational study design. In order to achieve my long-term goal of becoming an independent investigator and national leader in cancer disparities research, I require additional quantitative skills (large data analytics, machine learning-based risk prediction, unstructured data extraction using natural language processing), qualitative skills (effective scientific communication, scientific leadership), and professional development. Research Plan: The overarching research aim of this proposal is to develop a PErsonalized Risk Score for gastrIc CancEr (PRECISE) using real-world clinical data sources. My overall hypothesis is that through use of advanced data analytics and deep learning methods, a highly-refined cohort of individuals at highest risk for gastric cancer can be identified. The Specific Aims of this proposal seek to address this hypothesis: (1) to build a personalized risk prediction model using regression, (2) to build a personalized risk prediction model using machine learning algorithms, and (3) to compare regression and machine learning models in electronic health records data. Achievement of these aims will produce a novel, personalized prediction score which will help identify individuals at high risk for gastric cancer and who may benefit from targeted risk attenuation programs. Mentorship Team: To achieve these Aims, I have assembled a world class mentorship team with expertise in epidemiology and health disparities research (Latha Palaniappan, primary mentor), machine learning and natural language processing in EHR data (Tina Hernandez-Boussard, co-mentor), and gastric cancer screening and prevention (Joo Ha Hwang, co-mentor). Environment and Institutional Commitment: Stanford University is a world leader in clinical informatics, epidemiology, and health services research. I will have access to a unique data core, which contains one of the most extensive and robust collections of curated clinical data in the world. My mentorship team is committed to ensuring the success of the proposal, and in developing me to become an independent investigator competitive for R-level grants.

美国国家癌症研究所呼吁消除癌症发病率的差异