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January 14, 3 pm CT
A Bayesian Hierarchical Network for Combining Heterogeneous Data Sources in Medical Diagnoses, with Applications to COVID-19
Claire Donnat, Assistant Professor, Department of Statistics, University of Chicago
The increasingly widespread use of affordable, yet often less reliable medical data and diagnostic tools poses a new challenge for the field of Computer-aided Diagnosis: How can we combine multiple sources of information with varying levels of precision and uncertainty to provide an informative diagnosis estimate with confidence bounds? Motivated by a concrete application in lateral flow antibody testing, we devise a Stochastic Expectation-Maximization algorithm that allows the principled integration of heterogeneous and potentially unreliable data types. Our Bayesian formalism is essential in (a) flexibly combining these heterogeneous data sources and their corresponding levels of uncertainty, (b) quantifying the degree of confidence associated with a given diagnostic, and (c) dealing with the missing values that typically plague medical data. We quantify the potential of this approach on simulated data, and showcase its practicality by deploying it on a real COVID-19 immunity study.
Claire Donnat is an Assistant Professor in the Department of Statistics at the University of Chicago. Her work focuses on high-dimensional and Bayesian statistics and their applications to biomedical data. Prior to the University of Chicago, Donnat completed her PhD in Statistics at Stanford University, where she was advised by Professor Susan Holmes.
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