04-18, 10:20–10:55 (US/Eastern), Auditorium 3
In the era of big data, multi-modal data from multiple sources or modalities has become increasingly prevalent in various fields such as healthcare. The National COVID Cohort Collaborative (N3C) provides researchers with abundant clinical data in different forms by aggregating and harmonizing Electronic Health Records (EHR) data across different clinical organizations in the United States, making it convenient for researchers to analyze COVID-related topics and build models with large multimodal data. Bayesian risk analysis has advantages in handling the complexities and heterogeneities of multi-modal healthcare data, specifically in cohort studies when researchers try to answer questions of interest in public health or medicine field regarding COVID and Long COVID.
This talk is based on research projects by UVA iTHRIV on the N3C platform. Its target audience includes data scientists, undergraduate students, graduate students, researchers, and anyone interested in data science. The general structure of this talk will consist of a brief introduction to The National COVID Cohort Collaborative (N3C), a database with multi-modal data sets, quantitative methods and models in Bayesian risk analysis, and some real-world applications of these methods as well as some publications by our team. This talk will be informative and will include a balanced percentage of mathematical expressions and real-world applications, and the audience will learn more about quantitative methods to analyze multi-modal data in N3C.
No previous knowledge expected
Sihang Jiang is a PhD candidate at University of Virginia in systems engineering, and his research interests include Bayesian machine learning, Markov Chain Monte Carlo, AI for health, and natural language processing.