Skip to main content
SpringerLink
Log in

Healthcare Applications

  • Chapter
  • First Online:
Advances in Big Data Analytics

Abstract

Healthcare is also a very hot application area of data science, especially in the COVID-19 pandemic around the world since the beginning of 2020. This chapter provides two sections of the related healthcare applications. Section 11.1 deals with the evaluation of medical doctor’s performance by using ordinal regression-based approach [1], while Sect. 11.2 outlines a cutting-edge research finding to learn transmission patterns of COVID-19 outbreak by using an age-specific social contact characterization [2].

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://wjw.hubei.gov.cn/

  2. 2.

    http://wjw.beijing.gov.cn/

  3. 3.

    http://wsjk.tj.gov.cn/

  4. 4.

    http://wsjkw.hangzhou.gov.cn/

  5. 5.

    http://wsjkw.suzhou.gov.cn/

  6. 6.

    https://opendata.sz.gov.cn/data/data-Set/toDataDetails/29200_01503668

  7. 7.

    http://tjj.hubei.gov.cn/tjsj/sjkscx/tjnj/gsztj/whs/

  8. 8.

    http://tjj.beijing.gov.cn/

  9. 9.

    http://stats.tj.gov.cn/

  10. 10.

    http://tjj.hangzhou.gov.cn/

  11. 11.

    http://tjj.suzhou.gov.cn/

  12. 12.

    http://tjj.sz.gov.cn/

  13. 13.

    http://www.gov.cn/xinwen/2020-01/31/content_5473425.htm (Accessed on April 1, 2020).

  14. 14.

    http://www.dehenglaw.com/CN/tansuocontent/0008/017738/7.aspx?MID=0902 (Accessed on April 1, 2020).

  15. 15.

    http://www.hangzhou.gov.cn/art/2020/2/9/art_1256295_41893739.html(Accessed on April 1, 2020).

  16. 16.

    http://energy.people.com.cn/n1/2020/0213/c71661-31585079.html (Accessed on April 1, 2020).

  17. 17.

    http://www.sz.gov.cn/szzt2010/yqfk2020/szzxd/zczy/zcwj/fgzc/content/post_6728851.html (Accessed on April 1, 2020).

  18. 18.

    Shenzhen Municipal Data Open Platform: https://opendata.sz.gov.cn/data/data-Set/toDataDetails/29200_01503668

References

  1. Shi, Y., Li, P., Yu, X., Wang, H., Niu, L.: Evaluating doctor performance: ordinal regression-based approach. J. Med. Internet Res. 20(7), e240 (2018)

    Article  Google Scholar 

  2. Liu, Y., Gu, Z., Xia, S., Shi, B., Zhou, X.N., Shi, Y., Liu, J.: What are the underlying transmission patterns of covid-19 outbreak? An age-specific social contact characterization. EClinicalMedicine. 22, 100354 (2020)

    Article  Google Scholar 

  3. Hsieh, C.J., Chang, K.W., Lin, C.J., Keerthi, S.S., Sundararajan, S.: A dual coordinate descent method for large-scale linear svm. In: Proceedings of the 25th International Conference on Machine Learning, pp. 408–415 (2008)

    Chapter  Google Scholar 

  4. Xue, N.: Chinese word segmentation as character tagging. Int. J. Computat. Linguist. Chin. Lang. Process. 8(1) (2003) Special Issue on Word Formation and Chinese Language Processing, 29–48 (2003)

    Google Scholar 

  5. Zhang, Y., Jin, R., Zhou, Z.H.: Understanding bag-of-words model: a statistical framework. Int. J. Mach. Learn. Cybern. 1(1–4), 43–52 (2010)

    Article  Google Scholar 

  6. Witten, I.H., Frank, E.: Data mining: practical machine learning tools and techniques with java implementations. ACM SIGMOD Rec. 31(1), 76–77 (2002)

    Article  Google Scholar 

  7. Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 2001, 1189–1232 (2001)

    MathSciNet  MATH  Google Scholar 

  8. Chapelle, O., Shivaswamy, P., Vadrevu, S., Weinberger, K., Zhang, Y., Tseng, B.: Multi-task learning for boosting with application to web search ranking. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1189–1198 (2010)

    Chapter  Google Scholar 

  9. Kramer, S., Widmer, G., Pfahringer, B., De Groeve, M.: Prediction of ordinal classes using regression trees. Fundam. Inform. 47(1–2), 1–13 (2001)

    MathSciNet  MATH  Google Scholar 

  10. Chu, W., Keerthi, S.S.: New approaches to support vector ordinal regression. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 145–152 (2005)

    Chapter  Google Scholar 

  11. Chu, W., Keerthi, S.S.: Support vector ordinal regression. Neural Comput. 19(3), 792–815 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  12. Gu, B., Sheng, V.S., Tay, K.Y., Romano, W., Li, S.: Incremental support vector learning for ordinal regression. IEEE Trans. Neural Netw. Learn. Syst. 26(7), 1403–1416 (2014)

    Article  MathSciNet  Google Scholar 

  13. Herbrich, R., Graepel, T., Obermayer, K.: Support vector learning for ordinal regression (1999)

    Google Scholar 

  14. Frank, E., Hall, M.: A simple approach to ordinal classification. In: European Conference on Machine Learning, pp. 145–156 (2001)

    Google Scholar 

  15. Waegeman, W., Boullart, L.: An ensemble of weighted support vector machines for ordinal regression. Int. J. Comput. Syst. Sci. Eng. 3(1), 47–51 (2009)

    Google Scholar 

  16. Gutiérrez, P.A., Perez-Ortiz, M., Sanchez-Monedero, J., Fernandez-Navarro, F., Hervas-Martinez, C.: Ordinal regression methods: survey and experimental study. IEEE Trans. Knowl. Data Eng. 28(1), 127–146 (2015)

    Article  Google Scholar 

  17. Cardoso, J., da Costa, J.P.: Learning to classify ordinal data: the data replication method. J. Mach. Learn. Res. 8, 1393–1429 (2007)

    MathSciNet  MATH  Google Scholar 

  18. Gutiérrez, P.A., Pérez-Ortiz, M., Fernández-Navarro, F., Sánchez-Monedero, J., Hervás-Martínez, C.: An experimental study of different ordinal regression methods and measures. In: International Conference on Hybrid Artificial Intelligence Systems, pp. 296–307 (2012)

    Google Scholar 

  19. Diao, Q., Qiu, M., Wu, C.Y., Smola, A.J., Jiang, J., Wang, C.: Jointly modeling aspects, ratings and sentiments for movie recommendation (jmars). In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 193–202 (2014)

    Chapter  Google Scholar 

  20. Shimada, K., Endo, T.: Seeing several stars: a rating inference task for a document containing several evaluation criteria. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 1006–1014 (2008)

    Chapter  Google Scholar 

  21. Tang, D., Qin, B., Liu, T.: Document modeling with gated recurrent neural network for sentiment classification. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1422–1432 (2015)

    Chapter  Google Scholar 

  22. Kuo, T.M., Lee, C.P., Lin, C.J.: Large-scale kernel ranksvm. In: Proceedings of the 2014 SIAM International Conference on Data Mining, pp. 812–820 (2014)

    Google Scholar 

  23. Lee, C.P., Lin, C.J.: Large-scale linear ranksvm. Neural Comput. 26(4), 781–817 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  24. Kendall, M.G.: A new measure of rank correlation. Biometrika. 30(1/2), 81–93 (1938)

    Article  MATH  Google Scholar 

  25. Hamzah, F.B., Lau, C., Nazri, H., Ligot, D., Lee, G., Tan, C., Shaib, M., Zaidon, U., Abdullah, A., Chung, M., et al.: Coronatracker: worldwide COVID-19 outbreak data analysis and prediction. Bull. World Health Organ. 1(32) (2020)

    Google Scholar 

  26. Wu, J.T., Leung, K., Leung, G.M.: Nowcasting and forecasting the potential domestic and international spread of the 2019-ncov outbreak originating in Wuhan, China: a modelling study. Lancet. 395(10225), 689–697 (2020)

    Article  Google Scholar 

  27. Fumanelli, L., Ajelli, M., Manfredi, P., Vespignani, A., Merler, S.: Inferring the structure of social contacts from demographic data in the analysis of infectious diseases spread. PLoS Comput. Biol. 8(9), e1002673 (2012)

    Article  MathSciNet  Google Scholar 

  28. Xia, S., Liu, J., Cheung, W.: Identifying the relative priorities of subpopulations for containing infectious disease spread. PLoS One. 8(6), e65271 (2013)

    Article  Google Scholar 

  29. Mossong, J., Hens, N., Jit, M., Beutels, P., Auranen, K., Mikolajczyk, R., Massari, M., Salmaso, S., Tomba, G.S., Wallinga, J., et al.: Social contacts and mixing patterns relevant to the spread of infectious diseases. PLoS Med. 5(3), e74 (2008)

    Article  Google Scholar 

  30. Svensson, Å.: A note on generation times in epidemic models. Math. Biosci. 208(1), 300–311 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  31. Hethcote, H.W.: The mathematics of infectious diseases. SIAM Rev. 42(4), 599–653 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  32. Liu, T., Hu, J., Xiao, J., He, G., Kang, M., Rong, Z., Lin, L., Zhong, H., Huang, Q., Deng, A., et al.: Time-varying transmission dynamics of novel coronavirus pneumonia in china. BioRxiv. (2020)

    Google Scholar 

  33. Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., Ren, R., Leung, K.S., Lau, E.H., Wong, J.Y., et al.: Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N. Engl. J. Med. 382(13), 1199–1207 (2020)

    Article  Google Scholar 

  34. Epidemiology Working Group, et al.: Strategy and policy working group for NCIP epidemic response. Chinese Center for Disease Control and Prevention (2019)

    Google Scholar 

  35. Hellewell, J., Abbott, S., Gimma, A., Bosse, N., Jarvis, C., Russell, T.W., Munday, J.D., Kucharski, A.J., Edmunds, W.J., Funk, S., et al.: Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. Lancet Glob. Heal. 8, e488–e496 (2020)

    Article  Google Scholar 

  36. Sadique, M.Z., Adams, E.J., Edmunds, W.J.: Estimating the costs of school closure for mitigating an influenza pandemic. BMC Public Health. 8(1), 1–7 (2008)

    Article  Google Scholar 

  37. Hoang, T., Coletti, P., Melegaro, A., Wallinga, J., Grijalva, C.G., Edmunds, J.W., Beutels, P., Hens, N.: A systematic review of social contact surveys to inform transmission models of close-contact infections. Epidemiology (Cambridge, Mass.). 30(5), 723 (2019)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Chinese Academy of Sciences, Beijing, China

    Yong Shi

  2. University of Nebraska at Omaha, Omaha, Nebraska, USA

    Yong Shi

Authors
  1. Yong Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Shi, Y. (2022). Healthcare Applications. In: Advances in Big Data Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-16-3607-3_11

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-3607-3_11

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-3606-6

  • Online ISBN: 978-981-16-3607-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation