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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.

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来降低死亡率。胃癌仍然是分布最不均匀的癌症之一。美国的癌症，在某些种族、种族和移民群体中负担很高。鉴定对患胃癌风险最高的人的评估可能会允许有针对性的降低风险计划，并改善健康公平。应聘者和职业发展计划：我是一名获得董事会认证的胃肠病专家和硕士斯坦福大学受过学位培训的流行病学家，他试图利用数据科学来减少癌症后果。根据我的培训和经验，我在胃肠道癌症方面有丰富的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长。为了实现我的长期目标，成为癌症差异方面的独立调查者和全国领导者研究，我需要额外的量化技能(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：总体研究这项建议的目的是利用现实世界开发一个个性化的胃癌风险评分(精确)。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方法，可以确定高度精炼的胃癌高危人群。具体的该建议的目的在于解决这一假设：(1)使用以下方法构建个性化风险预测模型回归，(2)使用机器学习算法建立个性化风险预测模型，以及(3) 比较电子健康档案数据中的回归模型和机器学习模型。实现这些目标将产生一种新颖的、个性化的预测分数，有助于识别胃病的高危个体以及哪些人可能受益于有针对性的风险降低计划。导师团队：实现这些目标 AIMS，我组建了一支世界级的指导团队，在流行病学和健康差距方面拥有专业知识研究(Latha Palaniappan，主要导师)、机器学习和EHR数据中的自然语言处理 (Tina Hernandez-Boussard，共同导师)和胃癌筛查和预防(Joo Ha Hwang，共同导师)。环境和机构承诺：斯坦福大学在临床领域处于世界领先地位信息学、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和最强大的精选临床数据收集之一。我的导师团队是致力于确保提案的成功，并将我培养成一名独立调查员 R级助学金竞争激烈。