PRECISE - a PErsonalized Risk Score for gastrIc CancEr

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.