Reducing False-Positive Results in Newborn Screening Using Machine Learning

• Main Authors: Gang Peng (Author), Yishuo Tang (Author), Tina M. Cowan (Author), Gregory M. Enns (Author), Hongyu Zhao (Author), Curt Scharfe (Author)
• Format: Book
• Published: MDPI AG, 2020-03-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

Newborn screening (NBS) for inborn metabolic disorders is a highly successful public health program that by design is accompanied by false-positive results. Here we trained a Random Forest machine learning classifier on screening data to improve prediction of true and false positives. Data included...