標題: Newborn Screening for Phenylketonuria: Machine Learning vs Clinicians
作者: Chen, Wei-Hsin
Chen, Han-Ping
Tseng, Yi-Ju
Hsu, Kai-Ping
Hsieh, Sheau-Ling
Chien, Yin-Hsiu
Hwu, Wuh-Liang
Lai, Feipei
交大名義發表
National Chiao Tung University
關鍵字: Newborn screening;Tandem mass spectrometry;Support Vector Machine
公開日期: 2012
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.
URI: http://dx.doi.org/10.1109/ASONAM.2012.145
http://hdl.handle.net/11536/135430
ISBN: 978-0-7695-4799-2
978-1-4673-2497-7
DOI: 10.1109/ASONAM.2012.145
期刊: 2012 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM)
起始頁: 798
結束頁: 803
Appears in Collections:Conferences Paper