ArticleInternational journal of neonatal screening2020
Reducing False-Positive Results in Newborn Screening Using Machine Learning.
Article in International journal of neonatal screening, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed, 64 citations in OpenAlex.
- Newborn Screening in Saudi Arabia: Brief History, Current Practice, and Future Direction.International journal of neonatal screening · 2026Review
- Fully Automated Serum LC-MS/MS Platform and Pediatric Reference Intervals for Organic Acids, Amino Acids, and Acylcarnitines in Children (Ages 0-6 Years): Toward Quantitative Diagnosis of Inborn Errors of Metabolism.Diagnostics (Basel, Switzerland) · 2026Article
- Integrating the Genomic Revolution into Newborn Screening: Current Challenges and Future Perspectives.Pediatric reports · 2026Review
- Identification of potential anti-biofilm agents targeting LasR in Pseudomonas aeruginosa through machine learning-driven screening, molecular docking, and dynamics simulations.Scientific reports · 2025Article
- Improving newborn screening accuracy through genome sequencing, targeted metabolomics, and machine learning.BMC medical genomics · 2025Article
- Caregivers' Emotional Responses Triggered by a False-Positive VLCADD in Newborn Screening in Oita Prefecture.International journal of neonatal screening · 2025Article
- Effective algorithm to differentiate NBS MCADD cases from carriers and non-carriers and an assessment of the utility of the second newborn screen for MCADD.Molecular genetics and metabolism · 2025Article
- Propionyl Carnitine Metabolic Profile: Optimizing the Newborn Screening Strategy Through Customized Cut-Offs.Metabolites · 2025Article
- Integrating Machine Learning and Follow-Up Variables to Improve Early Detection of Hepatocellular Carcinoma in Tyrosinemia Type 1: A Multicenter Study.International journal of molecular sciences · 2025Article
- Beyond Targeted Newborn Screening: A Nontargeted Metabolomics Workflow to Investigate Birthweight-Metabolome Correlations.Analytical chemistry · 2025Article
- Digital-Tier Strategy Improves Newborn Screening for Glutaric Aciduria Type 1.International journal of neonatal screening · 2024Article
- Improving methylmalonic acidemia (MMA) screening and MMA genotype prediction using random forest classifier in two Chinese populations.European journal of medical research · 2024Article
- Improving the second-tier classification of methylmalonic acidemia patients using a machine learning ensemble method.World journal of pediatrics : WJP · 2024Article
- The Multi-Omic Approach to Newborn Screening: Opportunities and Challenges.International journal of neonatal screening · 2024Review
- Current Status of Newborn Bloodspot Screening Worldwide 2024: A Comprehensive Review of Recent Activities (2020-2023).International journal of neonatal screening · 2024Review
- Next-generation sequencing based newborn screening and comparative analysis with MS/MS.BMC pediatrics · 2024Article
- Newborn Screening for Inborn Errors of Metabolism by Next-Generation Sequencing Combined with Tandem Mass Spectrometry.International journal of neonatal screening · 2024Article
- The significance of machine learning in neonatal screening for inherited metabolic diseases.Frontiers in pediatrics · 2024Article
- Prediction of inherited metabolic disorders using tandem mass spectrometry data with the help of artificial neural networks.Turkish journal of medical sciences · 2024Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
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 39 metabolic analytes detected by tandem mass spectrometry and clinical variables such as gestational age and birth weight. Analytical performance was evaluated for a cohort of 2777 screen positives reported by the California NBS program, which consisted of 235 confirmed cases and 2542 false positives for one of four disorders: glutaric acidemia type 1 (GA-1), methylmalonic acidemia (MMA), ornithine transcarbamylase deficiency (OTCD), and very long-chain acyl-CoA dehydrogenase deficiency (VLCADD). Without changing the sensitivity to detect these disorders in screening, Random Forest-based analysis of all metabolites reduced the number of false positives for GA-1 by 89%, for MMA by 45%, for OTCD by 98%, and for VLCADD by 2%. All primary disease markers and previously reported analytes such as methionine for MMA and OTCD were among the top-ranked analytes. Random Forest's ability to classify GA-1 false positives was found similar to results obtained using Clinical Laboratory Integrated Reports (CLIR). We developed an online Random Forest tool for interpretive analysis of increasingly complex data from newborn screening.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.