Translated Titles

A Web-Service-Based Newborn Screening System for Metabolic Diseases





Key Words

網路服務 ; 新生兒篩檢 ; 串聯質譜儀 ; 醫療資訊系統 ; 新生兒代謝疾病 ; 資料探勘 ; Web-Based Services ; Newborn Screening ; Tandem Mass Spectrometry ; Hospital Information Systems ; Inborn Errors of Metabolism ; Data Mining



Volume or Term/Year and Month of Publication


Academic Degree Category




Content Language


Chinese Abstract


English Abstract

A Hospital Information System that integrates screening data and interpretation of the data is routinely requested. However, the accuracy of disease classification may be low because of the disease characteristics and analytes used for classification. The objective of this study is to describe a system that enhanced the neonatal screening system of the Newborn Screening Center at the National Taiwan University Hospital. The system has been designed and deployed based on a Service-Oriented Architecture framework under the Web Services .NET environment. The system consists of sample collection, testing, diagnosis, evaluation, treatment and follow-up services among collaborating hospitals. To improve the accuracy of newborn screening, machine learning and optimal feature selection mechanisms were investigated for screening newborns for inborn errors of metabolism. In this study, machine learning classification was used to predict the following: phenylketonuria, hypermethioninemia, and 3-methylcrotonyl-CoA-carboxylase deficiency. The classification methods used 435,682 newborn samples collected at the Center between 2006 and 2012. These samples include 229 newborns with values over the diagnostic cutoffs and 1822 over the screening cutoffs but that do not meet the diagnostic cutoffs. The feature selection strategies were defined as follows. The original 35 analytes and the manifested features are ranked based on the F-score. Next, the combinations of the top 20 ranked features were selected as input features to Support Vector Machines classifiers to obtain optimal feature sets. Finally, the feature sets were tested using 5-fold cross validation and the optimal models were generated. The datasets collected in year 2011 and 2012 were utilized as the predicting cases. By adopting the results of this study, the number of suspected cases could be reduced dramatically. Furthermore, the results of the research have been compared with those of other methodologies.

Topic Category 基礎與應用科學 > 資訊科學
醫藥衛生 > 醫藥總論
電機資訊學院 > 生醫電子與資訊學研究所
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