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：
10214927
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/10214927
关键词：
Achievement Address African American Age Alaska Native American Indians Asians Attention 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 Ethnic Origin Ethnic group Excision Face Funding Gastroenterologist General Population Goals Grant Health Services Research Healthcare Helicobacter pylori Hispanics Immigrant Individual Informatics Information Retrieval Inpatients Laboratories Lasso Lead Leadership Life Machine Learning Malignant Neoplasms Malignant neoplasm of gastrointestinal tract Master&apos s Degree 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 base 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 disparity health equity high risk improved improved outcome learning algorithm learning strategy machine learning algorithm malignant stomach neoplasm mortality multi-ethnic neural network novel outcome prediction personalized predictions personalized risk prediction prediction algorithm programs racial and ethnic racial diversity random forest risk prediction risk prediction model sex skills success supervised learning support vector machine tool unstructured data

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.

国家癌症研究所呼吁消除癌症发病率的差异通过使用数据科学的死亡率。胃癌仍然是最不合时宜的在美国，某些种族，种族和移民群体中的癌症燃烧很高。鉴别胃癌风险最大的个体可能允许有针对性的风险衰减计划，并改善健康平等。候选人和职业发展计划：我是董事会认证的胃肠病学家和硕士斯坦福大学学位培训的流行病学家，他试图使用数据科学来减少分布癌症的结果。根据我的培训和经验，我拥有胃肠癌的内容专业知识流行病学原理和观察性研究设计方面的诊断和方法学专业知识。为了实现我成为癌症分布的独立研究者和国家领导者的长期目标研究，我需要其他定量技能（大数据分析，基于机器学习的风险预测，使用自然语言处理的非结构化数据提取），定性技能（有效的科学沟通，科学领导）和专业发展。研究计划：总体研究该提案的目的是使用现实世界为Gastic Cancer（精确）开发个性化的风险评分临床数据来源。我的总体假设是，通过使用高级数据分析和深度学习可以鉴定出一种胃癌风险最高的个体的方法，可以确定。具体该提案的目的旨在解决这一假设：（1）使用回归，（2）使用机器学习算法构建个性化的风险预测模型，（3）比较电子健康记录数据中的回归和机器学习模型。实现这些目标将产生一个新颖的个性化预测评分癌症以及可能受益于有针对性的风险衰减计划。指导团队：实现这些 AIMS，我组建了一个世界一流的精通团队，该团队具有流行病学和健康分布方面的专业知识研究（Latha Palanippan，主要导师），EHR数据中的机器学习和自然语言处理（蒂娜·埃尔南德斯·布萨德（Tina Hernandez-Boussard），联合学者）和胃癌筛查和预防（Joo Ha Hwang，Co-Inseror）。环境和机构承诺：斯坦福大学是临床领域的世界领导者信息，流行病学和卫生服务研究。我将可以访问一个独特的数据核心，其中包含世界上最广泛，最强大的临床数据收集之一。我的攻击团队是致力于确保提案的成功，并在发展我成为独立调查员时 R级赠款的竞争力。