ArticleFrontiers in molecular biosciences2020
Improving the Diagnosis of Phenylketonuria by Using a Machine Learning-Based Screening Model of Neonatal MRM Data.
Article in Frontiers in molecular biosciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 20 citations in OpenAlex.
- Pushing the Boundaries of Biomarker Discovery in Phenylketonuria: Metabolomic Profiling Reveals Novel Biomarkers and Their Associations with Phenylalanine.Molecules (Basel, Switzerland) · 2026Article
- Enhancing TSH-based congenital hypothyroidism screening using machine learning and resampling algorithms.BMC medical informatics and decision making · 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
- Ethical and Practical Considerations of Artificial Intelligence in Pediatric Medicine: A Systematic Review.Cureus · 2025Review
- Fast Label-Free Metabolic Profile Recognition Identifies Phenylketonuria and Subtypes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- The significance of machine learning in neonatal screening for inherited metabolic diseases.Frontiers in pediatrics · 2024Article
- Machine Learning Methods Improve Specificity in Newborn Screening for Isovaleric Aciduria.Metabolites · 2023Article
- The effects of a self-learning package on mothers' knowledge and practices towards caring for their children with phenylketonuria.Journal of medicine and life · 2023Article
- Big Data in Laboratory Medicine-FAIR Quality for AI?Diagnostics (Basel, Switzerland) · 2022Review
- A multiplex protein panel assay for severity prediction and outcome prognosis in patients with COVID-19: An observational multi-cohort study.EClinicalMedicine · 2022Article
- Opportunities and challenges in machine learning-based newborn screening-A systematic literature review.JIMD reports · 2022Article
- Inferring Time-Lagged Causality Using the Derivative of Single-Cell Expression.International journal of molecular sciences · 2022Article
- Random forest classifier improving phenylketonuria screening performance in two Chinese populations.Frontiers in molecular biosciences · 2022Article
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8 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Phenylketonuria (PKU) is a common genetic metabolic disorder that affects the infant's nerve development and manifests as abnormal behavior and developmental delay as the child grows. Currently, a triple-quadrupole mass spectrometer (TQ-MS) is a common high-accuracy clinical PKU screening method. However, there is high false-positive rate associated with this modality, and its reduction can provide a diagnostic and economic benefit to both pediatric patients and health providers. Machine learning methods have the advantage of utilizing high-dimensional and complex features, which can be obtained from the patient's metabolic patterns and interrogated for clinically relevant knowledge. In this study, using TQ-MS screening data of more than 600,000 patients collected at the Newborn Screening Center of Shanghai Children's Hospital, we derived a dataset containing 256 PKU-suspected cases. We then developed a machine learning logistic regression analysis model with the aim to minimize false-positive rates in the results of the initial PKU test. The model attained a 95-100% sensitivity, the specificity was improved 53.14%, and positive predictive value increased from 19.14 to 32.16%. Our study shows that machine learning models may be used as a pediatric diagnosis aid tool to reduce the number of suspected cases and to help eliminate patient recall. Our study can serve as a future reference for the selection and evaluation of computational screening methods.
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