Literature DB >> 20703618

A newborn screening system based on service-oriented architecture embedded support vector machine.

Kai-Ping Hsu1, Sung-Huai Hsieh, Sheau-Ling Hsieh, Po-Hsun Cheng, Yung-Ching Weng, Jang-Hung Wu, Feipei Lai.   

Abstract

The 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.

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Year:  2009        PMID: 20703618     DOI: 10.1007/s10916-009-9305-6

Source DB:  PubMed          Journal:  J Med Syst        ISSN: 0148-5598            Impact factor:   4.460


  8 in total

Review 1.  Use of tandem mass spectrometry for multianalyte screening of dried blood specimens from newborns.

Authors:  Donald H Chace; Theodore A Kalas; Edwin W Naylor
Journal:  Clin Chem       Date:  2003-11       Impact factor: 8.327

2.  Screening for congenital adrenal hyperplasia: adjustment of 17-hydroxyprogesterone cut-off values to both age and birth weight markedly improves the predictive value.

Authors:  Bernhard Olgemöller; Adelbert A Roscher; Bernhard Liebl; Ralph Fingerhut
Journal:  J Clin Endocrinol Metab       Date:  2003-12       Impact factor: 5.958

3.  Secondary structure prediction with support vector machines.

Authors:  J J Ward; L J McGuffin; B F Buxton; D T Jones
Journal:  Bioinformatics       Date:  2003-09-01       Impact factor: 6.937

4.  Modelling of classification rules on metabolic patterns including machine learning and expert knowledge.

Authors:  Christian Baumgartner; Christian Böhm; Daniela Baumgartner
Journal:  J Biomed Inform       Date:  2005-04       Impact factor: 6.317

5.  A study on SMO-type decomposition methods for support vector machines.

Authors:  Pai-Hsuen Chen; Rong-En Fan; Chih-Jen Lin
Journal:  IEEE Trans Neural Netw       Date:  2006-07

6.  Quality performance of newborn screening systems: strategies for improvement.

Authors:  D Webster
Journal:  J Inherit Metab Dis       Date:  2007-08-14       Impact factor: 4.982

7.  Analysis of blood spot 17 alpha-hydroxyprogesterone concentration in premature infants--proposal for cut-off limits in screening for congenital adrenal hyperplasia.

Authors:  S Ohkubo; K Shimozawa; M Matsumoto; T Kitagawa
Journal:  Acta Paediatr Jpn       Date:  1992-04

8.  Two-tiered universal newborn screening strategy for severe combined immunodeficiency.

Authors:  Sean A McGhee; E Richard Stiehm; Morton Cowan; Paul Krogstad; Edward R B McCabe
Journal:  Mol Genet Metab       Date:  2005-11-02       Impact factor: 4.797

  8 in total

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