Reducing False-Positive Results in Newborn Screening Using Machine Learning
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...
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| Main Authors: | , , , , , |
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| Format: | Book |
| Published: |
MDPI AG,
2020-03-01T00:00:00Z.
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| Subjects: | |
| Online Access: | Connect to this object online. |
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