Presented by: Dr. Seyedehsaloumeh Sadeghzadeh

Abstract: Public health screening, which involves testing a large population for diseases using disease-related biomarkers, is an essential tool in a wide variety of settings, including newborn screening for genetic diseases. However, noisy information on the biomarker level, caused by external or subject-specific factors, introduces significant challenges to this problem. We design optimal data-driven biomarker screening policies to minimize subject misclassification errors, under noisy and uncertain biomarker measurements. Our case study on newborn screening for cystic fibrosis, which is based on a five-year data set from the North Carolina State Laboratory of Public Health, indicates that a substantial reduction in classification errors can be achieved using the proposed optimization-based models, over current practices.

Bio: Saloumeh Sadeghzadeh is an assistant professor in the School of Management at Binghamton University. Dr. Sadeghzadeh's research interests lie in applying stochastic modeling, optimization, and data analytics methodologies to problems arising in health care and public policy domains. 

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Linkedin: https://www.linkedin.com/in/saloumehsadeghzadeh