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dc.contributor.authorChen, Wei-Hsinen_US
dc.contributor.authorChen, Han-Pingen_US
dc.contributor.authorTseng, Yi-Juen_US
dc.contributor.authorHsu, Kai-Pingen_US
dc.contributor.authorHsieh, Sheau-Lingen_US
dc.contributor.authorChien, Yin-Hsiuen_US
dc.contributor.authorHwu, Wuh-Liangen_US
dc.contributor.authorLai, Feipeien_US
dc.date.accessioned2017-04-21T06:49:59Z-
dc.date.available2017-04-21T06:49:59Z-
dc.date.issued2012en_US
dc.identifier.isbn978-0-7695-4799-2en_US
dc.identifier.isbn978-1-4673-2497-7en_US
dc.identifier.urihttp://dx.doi.org/10.1109/ASONAM.2012.145en_US
dc.identifier.urihttp://hdl.handle.net/11536/135430-
dc.description.abstractThe metabolic disorders may hinder an infant\'s normal physical or mental development during the neonatal period. The metabolic diseases can be treated by effective therapies if the diseases are discovered in the early stages. Therefore, newborn screening program is essential to prevent neonatal from these damages. In the paper, a support vector machine (SVM) based algorithm is introduced in place of cut-off value decision to evaluate the analyte elevation raw data associated with Phenylketonuria. The data were obtained from tandem mass spectrometry (MS/MS) for newborns. In addition, a combined feature selection mechanism is proposed to compare with the cut-off scheme. By adapting the mechanism, the number of suspected cases is reduced substantially; it also handles the medical resources effectively and efficiently.en_US
dc.language.isoen_USen_US
dc.subjectNewborn screeningen_US
dc.subjectTandem mass spectrometryen_US
dc.subjectSupport Vector Machineen_US
dc.titleNewborn Screening for Phenylketonuria: Machine Learning vs Cliniciansen_US
dc.typeProceedings Paperen_US
dc.identifier.doi10.1109/ASONAM.2012.145en_US
dc.identifier.journal2012 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM)en_US
dc.citation.spage798en_US
dc.citation.epage803en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.identifier.wosnumberWOS:000320443500128en_US
dc.citation.woscount0en_US
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