Hsu, K.-P.K.-P.HsuHsieh, S.-H.S.-H.HsiehHsieh, S.-L.S.-L.HsiehCheng, P.-H.P.-H.ChengWeng, Y.-C.Y.-C.WengWu, J.-H.J.-H.WuFEI-PEI LAI2020-04-162020-04-162010https://scholars.lib.ntu.edu.tw/handle/123456789/484430The clinical symptoms of metabolic disorders are rarely apparent during the neonatal period, and if they are not treated earlier, irreversible damages, such as mental retardation or even death, may occur. Therefore, the practice of newborn screening is essential to prevent permanent disabilities in newborns. In the paper, we design, implement a newborn screening system using Support Vector Machine (SVM) classifications. By evaluating metabolic substances data collected from tandem mass spectrometry (MS/MS), we can interpret and determine whether a newborn has a metabolic disorder. In addition, National Taiwan University Hospital Information System (NTUHIS) has been developed and implemented to integrate heterogeneous platforms, protocols, databases as well as applications. To expedite adapting the diversities, we deploy Service-Oriented Architecture (SOA) concepts to the newborn screening system based on web services. The system can be embedded seamlessly into NTUHIS. ? 2009 Springer Science+Business Media, LLC.Newborn screening; Service-Oriented Architecture; Support Vector Machine; Tandem mass spectrometry; Web services[SDGs]SDG3[SDGs]SDG9hospital information system; metabolic disorder; newborn disease; newborn screening; review; support vector machine; Taiwan; tandem mass spectrometry; university hospital; Artificial Intelligence; Computer Communication Networks; Data Mining; Hospital Information Systems; Humans; Infant, Newborn; Metabolism, Inborn Errors; Neonatal Screening; Systems Integration; Taiwan; Tandem Mass Spectrometry; Workflowjournal article10.1007/s10916-009-9305-62-s2.0-77957566186https://www.scopus.com/inward/record.uri?eid=2-s2.0-77957566186&doi=10.1007%2fs10916-009-9305-6&partnerID=40&md5=1484b25d0a432d7ab6cf29668fe5b71e