Optimal Genetic Screening for Cystic Fibrosis
开发了一个决策支持模型，帮助在新生儿筛查中选择基因变异，平衡分类准确性和检测成本，并比较了两种常用筛查流程的优劣。
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.