Skip to main content
Springer Nature Link
Log in

A Newborn Screening System Based on Service-Oriented Architecture Embedded Support Vector Machine

  • Original Paper
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Tu, C.-M., Chang, H.-Y., Tang, M.-Y., Lai, F., et al., The design and implementation of a next generation information system for newborn screening, HEALTHCOM 2007, June, 2007.

  2. Hsieh, S.-h., Hsieh, S.-L., Chien, Y.-H., Wang, Z., and Lai, F.,A newborn screening system based on the service-oriented architecture. J. Med. Syst. Mar. 24, 2009; SCI, doi:10.1007/s10916-009-9265-x.

  3. Chace, D. H., Kalas, T. A., and Naylor, E. W., Use of tandem mass spectrometry for multianalyte screening of dried blood specimens from newborns. Clin. Chem. 49:1797–1817, 2003. doi:10.1373/clinchem.2003.022178.

    Article  Google Scholar 

  4. Pinheiro, M., Oliveira, J. L., Santos, M. A. S., Rocha, H., Cardoso, M. L., and Vilarinho, L., NeoScreen: A software application for MS/MS newborn screening analysis. In Biological and Medical Data Analysis (ISBMDA'2004), Lecture Notes in Computer Science—Volume 3337, Barcelona, Spain, 2004.

  5. Expanded Newborn Screening using Tandem Mass Spectrometry Financial, Ethical, Legal and Social Issues (FELSI), http://www.newbornscreening.info.

  6. Olgemoller, B., et al: Screening for congenital adrenal hyperplasia: adjustment of 17-hydroxyprogesterone cut-off values to both age and birth weight markedly improves the predictive value. J. Clin. Endocrinol. Metab. 88(12):5790–5794, 2003. doi:10.1210/jc.2002-021732.

    Article  Google Scholar 

  7. McGhee, S. A., Stiehm, E. R., Cowan, M., Krogstad, P. and McCabe, E. R. B., Two-tiered universal newborn screening strategy for severe combined immunodeficiency, Nov. 2, 2005; http://www.sciencedirect.com.

  8. Webster, D., Quality performance of newborn screening systems: Strategies for improvement. J. Inherit. Meta. Dis. 30:576–584, 2007.

    Article  MathSciNet  Google Scholar 

  9. Tu, C.-M., The New Generation of Information System for Newborn Screening—A Case Study of National Taiwan University Hospital, Dept. of Computer Science and Information Engineering, National Taiwan University, Taiwan, Master Thesis, June, 2007.

  10. Georgia Newborn Screening Manual for Metabolic Diseases and Hemoglobinopathies, Georgia Department of Human Resources, Division of Public Health, 2007.

  11. http://www.slh.wisc.edu/wps/scm/connect/extranet/, “Wisconsin Newborn Screening Laboratory.

  12. Pinheiro, M., Oliveira, J. L., Santos, M. A. S., Rocha, H., Cardoso, M. L., and Vilarinho, L., NeoScreen: A software application for MS/MS newborn screening analysis. In Biological and Medical Data Analysis (ISBMDA'2004), Lecture Notes in Computer Science—Volume 3337, Barcelona, Spain, 2004.

  13. Pinheiro, M., Oliveira, J. L., Santos, M. A. S., Rocha, H., Cardoso, M. L., and Vilarinho, L., A computer-based solution for screening of inherited metabolic diseases. J. Inherit. Metab. Dis. 27(Suppl. 1):4, 2004. (abstract).

    Google Scholar 

  14. Accelerated Technology Laboratories, Inc. http://www.atlab.com/Neomate.php. As of 09/01/2008

  15. Perkinelmer. http://www.perkinelmer.com/. As of 09/01/2008.

  16. Cortes, C., and Vapnik, V. (1995). Support-vector network.

  17. Chen, P. H., Fan, R. E., and Lin, C. J., A study on SMO-type decomposition methods for support vector machines, January 2005.

  18. Baumgartner, C., Böhm, C., and Baumgartner, D., Modelling of classification rules on metabolic patterns including machine learning and expert knowledge. J. Biomed. Inform. 38(2):89–98, 2005. doi:10.1016/j.jbi.2004.08.009.

    Article  Google Scholar 

  19. Meyer, D., Leisch, F., and Hornik, K., Support vector machines. Neurocomputing. 55(1–2):169–186, 2003.

    Article  Google Scholar 

  20. Michie, D., Spiegelhalter, D. J., Taylor, C. C., and Campbell, J., Machine learning, neural and statistical classification, 1995.

  21. Papazoglou, M. P., and van den Heuvel, W.-J., Service-oriented architectures: approaches, technologies and research issues. VLDB J. 16(3):389–415, 2007. doi:10.1007/s00778-007-0044-3.

    Article  Google Scholar 

  22. Papazoglou, M. P., Service-Oriented Computing: Concepts, characteristics and directions. Proceedings of the Fourth International Conference on Web Information Systems Engineering, p.3, December 10–12, 2003.

  23. Krafzig, D., Banke, K., and Slama, D., Enterprise SOA: Service oriented architecture best practices. Prentice-Hall, Englewood Cliffs, 2005.

    Google Scholar 

  24. Shepherd, M., Zitner, D., and Watters, C., “Medical portals: Web-based access to medical information. Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, pp. 1–10, 2000.

  25. Freudenstein, P., Nussbaumer, M., Majer, F., et al., A Workflow-Driven Approach for the Efficient Integration of Web Services in Portals, Services Computing, SCC 2007, IEEE International Conference, pp. 410–417, 2007.

  26. Murray, M., Strategies for the successful implementation of workflow systems within healthcare: a cross case comparison, System Sciences, 2003. Proceedings of the 36th Annual Hawaii International Conference, pp. 10, 2003.

  27. Bunge, R., Chung, S., and Endicott-Popovsky et al., An Operational Framework for Service Oriented Architecture Network Security”, Hawaii International Conference on System Sciences, Proceedings of the 41st Annual, pp. 312–312, 2008.

  28. Lewis, G. A., Morris, E., Simanta, S., et al., Common Misconceptions about Service-Oriented Architecture, Commercial-off-the-Shelf (COTS)-Based Software Systems, ICCBSS ‘07, Sixth International IEEE Conference, pp. 123–130, 2007.

  29. World Wide Web Consortium (W3C), “(SOAP) specifications” http://www.w3.org/TR/soap/, Feb, 2006.

  30. World Wide Web Consortium (W3C), “WSDL Note Version 1.1,” 15 March, 2001, http://www.w3c.org/TR/wsdl/.

  31. Ward, J. J., McGuffin, L. J., Buxton, B. F., and Jones, D. T., Secondary structure prediction with support vector machine. Bioinformatics. 19:1650–1655, 2003. doi:10.1093/bioinformatics/btg223.

    Article  Google Scholar 

  32. Forthofer, N., Lee, E. S., and Hernandez, M., Biostatistics, Second Edition: A Guide to Design, Analysis and Discovery, 2006.

  33. A Library for Support Vector Machines: http://www.csie.ntu.edu.tw/∼cjlin/libsvm/index.html.

  34. Ohkubo, S., Shimozawa, K., Matsumoto, M., and Kitagawa, T., Analysis of blood spot 17α-hydroxyprogesterone concentration in premature infants—proposal for cut-off limits in screening for congenital adrenal hyperplasia. Acta Paediatr. Jpn. 34:126–133, 1992.

    Google Scholar 

  35. Health Information Privacy. http://www.hhs.gov/ocr/privacy/index.html.

Download references

Acknowledgments

The authors would like to acknowledge members of the Pediatrics and Medical Genetics Office, the Information Systems Office at NTUH for their assistance.

Author information

Authors and Affiliations

  1. Information Systems Office, National Taiwan University Hospital, No.7, Chung-San South Road, Taipei, Taiwan

    Sung-Huai Hsieh, Sheau-Ling Hsieh, Yung-Ching Weng & Feipei Lai

  2. Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan

    Kai-Ping Hsu, Sung-Huai Hsieh, Yung-Ching Weng & Feipei Lai

  3. Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan

    Feipei Lai

  4. Network and Computer Centre, National Chiao Tung University, Hsin Chu, Taiwan

    Sheau-Ling Hsieh & Jang-Hung Wu

  5. Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan

    Feipei Lai

  6. Software Engineering Department, National Kaohsiung Normal University, Kaohsiung, Taiwan

    Po-Hsun Cheng

Authors
  1. Kai-Ping Hsu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Sung-Huai Hsieh
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Sheau-Ling Hsieh
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Po-Hsun Cheng
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Yung-Ching Weng
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Jang-Hung Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Feipei Lai
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Sung-Huai Hsieh.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hsu, KP., Hsieh, SH., Hsieh, SL. et al. A Newborn Screening System Based on Service-Oriented Architecture Embedded Support Vector Machine. J Med Syst 34, 899–907 (2010). https://doi.org/10.1007/s10916-009-9305-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-009-9305-6

Keywords