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Associative Feature Information Extraction Using Text Mining from Health Big Data

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Abstract

With the development of big data computing technology, most documents in various areas, including politics, economics, society, culture, life, and public health, have been digitalized. The structure of conventional documents differs according to their authors or the organization that generated them. Therefore, policies and studies related to their efficient digitalization and use exist. Text mining is the technology used to classify, cluster, extract, search, and analyze data to find patterns or features in a set of unstructured or structured documents written in natural language. In this paper, a method for extracting associative feature information using text mining from health big data is proposed. Using health documents as raw data, health big data are created by means of the Web. The useful information contained in health documents is extracted through text mining. Health documents as raw data are collected through Web scraping and then saved in a file server. The collected raw data of health documents are sentence type, and thus morphological analysis is applied to create a corpus. The file server executes stop word removal, tagging, and the analysis of polysemous words in a preprocessing procedure to create a candidate corpus. TF-C-IDF is applied to the candidate corpus to evaluate the importance of words in a set of documents. The words classified as of high importance by TF-C-IDF are included in a set of keywords, and the transactions of each document are created. Using an Apriori mining algorithm, the association rules of keywords in the created transaction are analyzed and associative keywords are generated. TF-C-IDF weights and associative keywords are extracted from health big data as associative features. The proposed method is a base technology for creating added value in the healthcare industry in the era of the 4th industrial revolution. Its evaluation in terms of F-measure and efficiency showed its performance to be high. The method is expected to contribute to healthcare big data management and information search.

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References

  1. Jung, H., & Chung, K. (2016). Life style improvement mobile service for high risk chronic disease based on PHR platform. Cluster Computing, 19(2), 967–977.

    Article  Google Scholar 

  2. HL7, Health Level Seven International. http://www.hl7.org/.

  3. Kim, J. C., & Chung, K. (2017). Depression index service using knowledge based crowdsourcing in smart health. Wireless Personal Communication, 93(1), 255–268.

    Article  Google Scholar 

  4. Yoo, H., & Chung, K. (2017). PHR based diabetes index service model using life behavior analysis. Wireless Personal Communications, 93(1), 161–174.

    Article  Google Scholar 

  5. Chung, K., & Park, R. C. (2016). PHR open platform based smart health service using distributed object group framework. Cluster Computing, 19(1), 505–517.

    Article  Google Scholar 

  6. Kang, H. C. (2016). National-level use of health care big data and its policy implications. Health Welf Policy Forum, 238, 55–71.

    Google Scholar 

  7. Yoo, H., & Chung, K. (2017). Heart rate variability based stress index service model using bio-sensor. Cluster Computing. https://doi.org/10.1007/s10586-017-0879-3.

    Article  Google Scholar 

  8. Jung, H., & Chung, K. (2016). Knowledge-based dietary nutrition recommendation for obese management. Information Technology and Management, 17(1), 29–42.

    Article  Google Scholar 

  9. Health Insurance Review and Assessment Service (HIRA). http://opendata.hira.or.kr/.

  10. Shmueli, G., Patel, N. R., & Bruce, P. C. (2016). Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner. Wiley.

  11. Kim, J. S. (2016). Emotion prediction of paragraph using big data analysis. Journal of Digital Convergence, 14(11), 267–273.

    Article  Google Scholar 

  12. Ravichandran, D., & Hovy, E. (2002). Learning surface text patterns for a question answering system. In Proceedings of the annual meeting on association for computational linguistics (pp. 41–47).

  13. Bernth, A., Gdaniec, C. M., McCord, M. C., & Medeiros, S. A. (2001). System and method for estimating accuracy of an automatic natural language translation. U.S. Patent, No. 6,285,978.

  14. Song, C. W., Jung, H., & Chung, K. (2017). Development of a medical big-data mining process using topic modeling. Cluster Computing. https://doi.org/10.1007/s10586-017-0942-0.

    Article  Google Scholar 

  15. Eagle, N., & Pentland, A. S. (2006). Reality mining: Sensing complex social systems. Personal and Ubiquitous Computing, 10(4), 255–268.

    Article  Google Scholar 

  16. Tan, A. H. (1999). Text mining: The state of the art and the challenges. In Proceedings of the PAKDD 1999 workshop on knowledge discovery from advanced databases (Vol. 8, pp. 65–70).

  17. Pak, A., & Paroubek, P. (2010). Twitter as a corpus for sentiment analysis and opinion mining. In Proceedings of the international conference on language resources and evaluation (pp. 1320–1326).

  18. Bonchi, F., Castillo, C., Gionis, A., & Jaimes, A. (2011). Social network analysis and mining for business applications. ACM Transactions on Intelligent Systems and Technology, 2(3), 22.

    Article  Google Scholar 

  19. Tan, P. N., Steinbach, M., & Kumar, V. (2006). Introduction to Data Mining, Addison Wesley.

  20. Park, D. S., Moon, Y. S., Park, Y. H., Youn, C. H., Jung, Y. S., & Jang, H. S. (2014). Bigdata computing technology. Bengaluru: Hanbit Academy.

    Google Scholar 

  21. Pillbox. http://pillbox.nlm.nih.gov/.

  22. National Library of Medicine. http://www.nlm.nih.gov/.

  23. Van De Belt, T. H., Engelen, L. J., Berben, S. A., & Schoonhoven, L. (2010). Definition of health 2.0 and medicine 2.0: A systematic review. Journal of Medical Internet Research, 12(2), e18.

    Article  Google Scholar 

  24. Go, S. J., & Jung, Y. H. (2012). Health risk prediction using big health data. Health and Welfare Policy Forum, 193, 43–52.

    Google Scholar 

  25. Drug Information Portal. https://druginfo.nlm.nih.gov/.

  26. DailyMed. https://dailymed.nlm.nih.gov/.

  27. Cui, W., Wu, Y., Liu, S., Wei, F., Zhou, M. X., & Qu, H. (2010). Context preserving dynamic word cloud visualization. In Pacific visualization symposium (pp. 121–128).

  28. The R Project for Statistical Computing. https://www.r-project.org/.

  29. rvest. https://cran.r-project.org/web/packages/rvest/.

  30. Brown, P. F., Desouza, P. V., Mercer, R. L., Pietra, V. J. D., & Lai, J. C. (1992). Class-based N-gram models of natural language. Computational Linguistics, 18(4), 467–479.

    Google Scholar 

  31. tm Package. https://cran.r-project.org/web/packages/tm/.

  32. Aizawa, A. (2003). An information-theoretic perspective of TF–IDF measures. Information Processing and Management, 39(1), 45–65.

    Article  MATH  Google Scholar 

  33. Han, J., Pei, J., & Kamber, M. (2011). Data mining: Concepts and techniques. New York City, NY: Elsevier.

    MATH  Google Scholar 

  34. Weka 3. http://www.cs.waikato.ac.nz/~ml/weka/.

  35. Chung, K. Y., Na, Y., & Lee, J. H. (2013). Interactive design recommendation using sensor based smart wear and weather WebBot. Wireless Personal Communications, 73(2), 243–256.

    Article  Google Scholar 

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Acknowledgements

This work was supported by Kyonggi University Research Grant 2017.

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Authors and Affiliations

  1. Data Mining Laboratory, Department of Computer Science, Kyonggi University, 154-42, Gwanggyosan-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16227, Korea

    Joo-Chang Kim

  2. Division of Computer Science and Engineering, Kyonggi University, 154-42, Gwanggyosan-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16227, Korea

    Kyungyong Chung

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  1. Joo-Chang Kim
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Correspondence to Kyungyong Chung.

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Kim, JC., Chung, K. Associative Feature Information Extraction Using Text Mining from Health Big Data. Wireless Pers Commun 105, 691–707 (2019). https://doi.org/10.1007/s11277-018-5722-5

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