Developing an explainable machine learning model to predict false-negative citrin deficiency cases in newborn screening
An interpretable machine learning model utilizing metabolite and demographic data enhances the detection of false-negative NICCD cases, facilitates early identification and intervention, and ultimately improves the overall effectiveness of the newborn screening system.
Authors
- Pingping Ge
- Ziyan Cen
- Lingwei Hu (ORCID: https://orcid.org/0000-0001-8442-0579)
- Qimin He (ORCID: https://orcid.org/0000-0001-8262-1035)
- Yuhe Chen (ORCID: https://orcid.org/0009-0006-6632-8895)
- Haomin Li (ORCID: https://orcid.org/0000-0002-6420-7719)
- Peiyao Wang (ORCID: https://orcid.org/0000-0002-2473-8777)
- Xinjie Yang
- Xinwen Huang (ORCID: https://orcid.org/0000-0002-2799-9155)
- Benqing Wu (ORCID: https://orcid.org/0000-0003-4126-7468)
Institutions
- Suzhou University of Science and Technology (CN)
- Children's Hospital of Zhejiang University (CN)
- Shenzhen Children's Hospital (CN)
Publication Details
- Journal
- Orphanet Journal of Rare Diseases
- Published
- 2025-10-08
- DOI
- https://doi.org/10.1186/s13023-025-04045-z
- Primary Topic
- Metabolism and Genetic Disorders
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China