Newborn screening for phenylketonuria: Machine learning vs clinicians

Wei Hsin Chen, Han Ping Chen, Yi Ju Tseng, Kai Ping Hsu, Sheau Ling Hsieh, Yin Hsiu Chien, Wuh Liang Hwu, Feipei Lai

研究成果: 書貢獻/報告類型會議論文篇章同行評審

4 引文 斯高帕斯(Scopus)

摘要

The 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.

原文???core.languages.en_GB???
主出版物標題Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012
頁面798-803
頁數6
DOIs
出版狀態已出版 - 2012
事件2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012 - Istanbul, Turkey
持續時間: 26 8月 201229 8月 2012

出版系列

名字Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012

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???event.eventtypes.event.conference???2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012
國家/地區Turkey
城市Istanbul
期間26/08/1229/08/12

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