Affiliation:
1. Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia 24061;
2. Department of Information Systems, Statistics, and Management Science, University of Alabama, Tuscaloosa, Alabama 35487
Abstract
Improving Newborn Screening for Genetic Diseases Screening newborns for life-threatening genetic diseases is an important public health initiative. Cystic fibrosis is one of the most prevalent diseases in this context. As part of the cystic fibrosis screening process, all states in the United States use multiple tests, including genetic tests that detect a subset of the more than 300 genetic variants (specific mutations) that cause cystic fibrosis. In “Optimal Genetic Screening for Cystic Fibrosis,” El-Hajj, D.R. Bish, and E.K. Bish develop a decision support model to select which genetic variants to screen for, considering the trade-off between classification accuracy and testing cost, and the technological constraints that limit the number of variants selected. Because variant prevalence rates are highly uncertain, a robust optimization framework is developed. Further, two commonly used cystic fibrosis screening processes are analytically compared, and conditions under which each process dominates are established. A case study based on published data are provided.
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Subject
Management Science and Operations Research,Computer Science Applications
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献