Publications by authors named "Xiuru Chen"
Article Synopsis
- Gas chromatography-mass spectrometry (GC-MS) is an effective method for urine analysis, but its application for screening inborn errors of metabolism (IEM) is limited due to the rarity of IEM and the complexity of data interpretation.
- A machine learning model based on 355,197 GC-MS test cases from China was developed to better identify and classify rare IEMs, using techniques like undersampling and oversampling to handle imbalanced data.
- The proposed model demonstrates high sensitivity and accuracy in identifying specific IEMs, suggesting that machine learning can significantly enhance the interpretation and efficiency of GC-MS for IEM screening.
Background: Inherited metabolic disorders (IMDs) usually occurs at young age and hence it severely threatening the health and life of young people. While so far there lacks a comprehensive study which can reveals China's nationwide landscape of IMDs. This study aimed to evaluate IMDs incidence and regional distributions in China at a national and province level to guide clinicians and policy makers.
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