Skip to main content
SpringerLink
Log in

RETRACTED ARTICLE: Development of a medical big-data mining process using topic modeling

  • Published:
Cluster Computing Aims and scope Submit manuscript

This article was retracted on 05 December 2022

This article has been updated

Abstract

With the development of convergence information technology, all of the spaces and objects of human living have become digitized. In the health- and medical-service areas, IT supports Internet of things (IoT)-based medical services and health-care systems for patients. Medical facilities have been advanced on the basis of such IoT devices, and the digitized information on human behaviors and health makes the delivery of efficient and convenient health care possible. Under the given circumstances, health and medical care have been researched. For some of this research, the patient-health data were collected using IoT-based medical devices, and they served as a tool for medical diagnosis and treatment. This study proposes the development of a medical big-data mining process for which topic modeling is employed. The proposed method uses the big data that are offered by the open system of the health- and medical-services big data from the Health Insurance Review and Assessment Service, and their application follows the guidelines of the knowledge discovery in big-data process for data mining and topic modeling. For the medical data regarding the topic modeling, the public structured health- and medical-services big data, Open API, and patient datasets were used. For the document classification in the semantic situation of a topic, the Bag of Words technique and the latent Dirichlet allocation method were applied to find the document association for the development of the medical big-data mining process. In addition, this study conducted a performance evaluation of the topic-modeling accuracy based on the medical big-data mining process and the topic-modeling efficiency, and the effectiveness of the proposed method was examined.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

Change history

Notes

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

  2. Ministry of Health and Welfare, http://www.mohw.go.kr/eng/.

References

  1. Jung, H., Chung, K.: PHR based life health index mobile service using decision support model. Wirel. Pers. Commun. 86(1), 315–332 (2016)

    Article  Google Scholar 

  2. Jung, H., Chung, K.Y., Lee, Y.H.: Decision supporting method for chronic disease patients based on mining frequent pattern. Multimed. Tools Appl. 74(20), 8979–8991 (2015)

    Article  Google Scholar 

  3. Park, D., Kim, J., Kim, J., Jung, E., Lee, Y.: U-health service model for managing health of chronic patients in multi-platform environment. J. Korea Contents Assoc. 11(8), 23–32 (2011)

    Article  Google Scholar 

  4. Jung, H., Chung, K.: Sequential pattern profiling based bio-detection for smart health service. Clust. Comput. 18(1), 209–219 (2015)

    Article  Google Scholar 

  5. Jung, H., Chung, K.: Knowledge-based dietary nutrition recommendation for obese management. Inf. Technol. Manag. 17(1), 29–42 (2016)

    Article  Google Scholar 

  6. Kim, J.H., Chung, K.Y.: Ontology-based healthcare context information model to implement ubiquitous environment. Multimed. Tools Appl. 71(2), 873–888 (2014)

    Article  Google Scholar 

  7. Pollitt, M., Whitledge, A.: Exploring big haystacks. Adv. Digit. Forensics II, 67–76 (2006)

    Article  Google Scholar 

  8. Song, C.W.: Text mining process model for evidence collection and analysis in digital forensic investigation. PhD Thesis, Inha University (2016)

  9. Meng, C-r., Zhang, H-l., Zeng, L-f., Li, Z-p., Huang, J., Liang, Z.: Evidence-based decision support for the clinical practice of acupuncture: data mining approaches. In: Proceedings of IEEE International Conference on Bioinformatics and Biomedicine, pp. 180–181 (2013)

  10. Lao, Y.R., Li, Y., Li, S.C., Gu, Q.Z., Yang, Z., Liang, Z.H., Tan, D.Y., Fan, Y.P.: A data mining research method based on the concept of evidence based TCM inheritance in famous veteran TCM doctors’ personal medical records. In: Proceedings of IEEE International Conference on Bioinformatics and Biomedicine Workshops, pp. 746–748 (2011)

  11. Jung, H., Yoo, H., Chung, K.: Associative context mining for ontology-driven hidden knowledge discovery. Clust. Comput. 19(4), 2261–2271 (2016)

    Article  Google Scholar 

  12. Venter, J., Waal, A., Willers, C.: Specializing CRISP-DM for evidence mining. In: Advances in Digital Forensics, pp. 303–315. Springer, Boston (2007)

  13. Beebe, N., Dietrich, G.: A new process model for text string searching. Adv. Digit. Forensics III, 73–85 (2007)

    Google Scholar 

  14. McCue, C.: Data Mining and Predictive Analysis: Intelligence Gathering, pp. 237–253. Butterworth-Heinemann, Waltham (2014)

    Google Scholar 

  15. Chung, K., Park, R.C.: PHR open platform based smart health service using distributed object group framework. Clust. Comput. 19(1), 505–517 (2016)

    Article  Google Scholar 

  16. Kim, J.C., Chung, K.: Depression index service using knowledge based crowdsourcing in smart health. Wirel. Pers. Commun. 93(1), 255–268 (2017)

    Article  Google Scholar 

  17. Yoo, H., Chung, K.: PHR based diabetes index service model using life behavior analysis. Wirel. Pers. Commun. 93(1), 161–174 (2017)

    Article  Google Scholar 

  18. Song, C.W., Chung, K., Lee, J.H.: Catching up faster data in digital crime using mobile devices. Multimed. Tools Appl. 74(20), 9007–9016 (2015)

    Article  Google Scholar 

  19. Chung, K., Kim, J.C., Park, R.C.: Knowledge-based health service considering user convenience using hybrid Wi-Fi P2P. Inf. Technol. Manag. 17(1), 67–80 (2016)

    Article  Google Scholar 

  20. Kim, S.H., Chung, K.: Emergency situation monitoring service using context motion tracking of chronic disease patients. Clust. Comput. 18(2), 747–759 (2016)

    Article  Google Scholar 

  21. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Databases, pp. 487–499 (1994)

  22. Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of ACM SIGMOD on Management of Data, pp. 207–216 (1993)

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

  24. Chung, K., Na, Y., Lee, J.H.: Interactive design recommendation using sensor based smart wear and weather WebBot. Wirel. Pers. Commun. 73(2), 243–256 (2013)

    Article  Google Scholar 

  25. Jung, H., Chung, K.: Life style improvement mobile service for high risk chronic disease based on PHR platform. Clust. Comput. 19(2), 967–977 (2016)

    Article  Google Scholar 

  26. Mei, Q., Zhai, C.: Discovering evolutionary theme patterns from text: an exploration of temporal text mining. In: ACM SIGKDD International Conference, pp. 198–207 (2005)

  27. Griffiths, T.L., Steyvers, M., Tenenbaum, J.B.: Topics in semantic representation. Psychol. Rev. 114, 211–244 (2007)

    Article  Google Scholar 

  28. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  29. Csurka, G., Dance, C., Fan, L.X., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: ECCV, pp. 1–14 (2004)

  30. Kim, J.C., Jung, H., Chung, K.: Mining based urban climate disaster index service according to potential risk. Wirel. Pers. Commun. 89(3), 1009–1025 (2016)

    Article  Google Scholar 

  31. Blei, D.M.: Probabilistic topic models. Commun. ACM 55, 77–84 (2012)

    Article  Google Scholar 

  32. Porteous, I., Newman, D., Ihler, A., Asuncion, A., Smyth, P., Welling, M.: Fast collapsed Gibbs sampling for latent Dirichlet allocation. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 569–577 (2008)

  33. Waal, A., Venter, J., Barnard, E.: Applying topic modeling to forensic data. In: Advances in Digital Forensics, pp. 115–126. Springer, Boston (2008)

  34. Korea Centers for Disease Control and Prevention: 6th Korean National Health and Nutrition Examinations Survey (KNHANES VI-1). Korea Centers for Disease Control and Prevention (2015)

  35. Jung, H., Chung, K.: Ontology-driven slope modeling for disaster management service. Clust. Comput. 18(2), 677–692 (2015)

    Article  Google Scholar 

  36. Kim, J.C., Jung, H., Yoo, H., Kim, J.H., Chung, K.: Medical mining based silver smart platform for elderly health. In: Proceedings of the 4th International Conference for Small and Medium Business, 2017, pp. 356–357 (2017)

Download references

Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A1A09917313).

Author information

Authors and Affiliations

  1. Department of Computer and Information Engineering, Inha University, 100 Inha-ro, Nam-gu, Inchon, 22212, Korea

    Chang-Woo Song

  2. Division of Data Analysis & Development, Local Information Institute, 2, Sicheong-ro, Wonju-si, Gangwon-do, 26387, Korea

    Hoill Jung

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

    Kyungyong Chung

Authors
  1. Chang-Woo Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hoill Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kyungyong Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kyungyong Chung.

Additional information

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s10586-022-03869-9"

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Song, CW., Jung, H. & Chung, K. RETRACTED ARTICLE: Development of a medical big-data mining process using topic modeling. Cluster Comput 22 (Suppl 1), 1949–1958 (2019). https://doi.org/10.1007/s10586-017-0942-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-0942-0

Keywords

Search

Navigation