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Topical Prescriptive Analytics System for Automatic Recommendation of Convergence Technology

  • Research Paper
  • Biomedical Engineering
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Abstract

This study applies text mining in scientific articles for discovery of interdisciplinary convergence technology between biotechnology (BT) and information and communication technology (ICT). For in-depth interpretation of the technologies without domain experts’ review, a topic modeling method, Latent Dirichlet allocation (LDA), was used to propose an automatic recommendation system. We also applied prescriptive analytics with an option for users to select appropriate recommendation process of items. Our findings are as follows. First, LDA was efficient to facilitate the analysis of a large collection of documents by decreasing the dimension of the data. Second, the automatic recommendation method with various selectable options that could overcome limitations from that domain experts review the entire set of numerous topics. Finally, as a result of investigation of the final convergence technology candidates, it was proved that the system we propose here is more cost/time-effective compared to a method of reviewing all of the topic associations. Overall, a new methodology to support experts’ final decision by LDAand prescriptive analytics-based automatic recommendation system was successfully developed to discover convergence technologies between BT and ICT, which was also proved by several examples of applications.

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References

  1. MIT Technology Review, 10 Breakthrough Technologies. http://www.technologyreview.com/lists/technologies/2015/.

  2. McKinsey Global Institute, Big data: The next frontier for innovation, competition, and productivity. http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/big-data-the-next-frontier-for-innovation.

  3. Hofmann, T. (1999) Probabilistic latent semantic indexing. Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. August 15–19. Berkeley, USA.

    Book  Google Scholar 

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

    Google Scholar 

  5. Lu, Y., S. Okada, and K. Nitta (2013) Semi-supervised latent Dirichlet allocation for multi-label text classification. pp. 351–360. In: M. Ali, T. Bosse, K. V. Hindriks, M. Hoogendoorn, C. M. Jonker, and J. Treur (eds.). Proceedings of the Recent Trends in Applied Artificial Intelligence: 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent System. Springer, Berlin, Heidelberg, Germany.

    Chapter  Google Scholar 

  6. Vulic, I., W. De Smet, and M. F. Moens (2013) Cross-language information retrieval models based on latent topic models trained with document-aligned comparable corpora. Inf. Retr. Boston. 16: 331–368.

    Article  Google Scholar 

  7. Kim, D. and A. Oh (2011) Topic chains for understanding a news corpus. pp. 163–176. In: A. Gelbukh (ed.). Computational Linguistics and Intelligent Text Processing. Springer, Berlin, Heidelberg, Germany.

    Chapter  Google Scholar 

  8. Jeong, D. H. and M. Song (2014) Time gap analysis by the topic model-based temporal technique. J. Informetr. 8: 776–790.

    Article  Google Scholar 

  9. Xu, S., L. Zhu, X. Qiao, Q. Shi, and J. Gui (2012) Topic linkages between papers and patents. pp. 176–183. In: Proceedings of the 2012 International Conference on Advanced Science and Technology. SERSC, Daejeon, Korea.

    Google Scholar 

  10. Vretos, N., N. Nikolaidis, and I. Pitas (2012) Video fingerprinting using Latent Dirichlet Allocation and facial images. Pattern Recognit. 45: 2489–2498.

    Article  Google Scholar 

  11. Li, F., S. R. Zhou, J. M. Zhang, D. Y. Zhang, and L. Y. Xiang (2013) Attribute-based knowledge transfer learning for human pose estimation. Neurocomputing. 116: 301–310.

    Article  Google Scholar 

  12. Farrahi, K. and D. Gatica-Perez (2012) Extracting mobile behavioral patterns with the distant N-Gram topic model. Proceedings of the 2012 16th International Symposium on Wearable Computers (ISWC). June 18–22. Newcastle, UK.

    Google Scholar 

  13. Wang, H., Y. Ding, J. Tang, X. Dong, B. He, J. Qiu, and D. J. Wild (2011) Finding complex biological relationships in recent PubMed articles using Bio-LDA. PLoS One. 6: e17243.

    Article  CAS  Google Scholar 

  14. Cohen, R., I. Aviram, M. Elhadad, and N. Elhadad (2014) Redundancy-aware topic modeling for patient record notes. PLoS One. 9: e87555.

    Article  Google Scholar 

  15. Yu, Z., E. Bernstam, T. Cohen, B. C. Wallace, and T. R. Johnson (2016) Improving the utility of MeSH® terms using the TopicalMeSH representation. J. Biomed. Inform. 61: 77–86.

    Article  Google Scholar 

  16. Huang, Z., X. Lu, and H. Duan (2013) Latent treatment pattern discovery for clinical processes. J. Med. Syst. 37: 9915.

  17. Lu, H. M., C. P. Wei, and F. Y. Hsiao (2016) Modeling healthcare data using multiple-channel latent Dirichlet allocation. J. Biomed. Inform. 60: 210–223.

    Article  Google Scholar 

  18. Wang, T., Z. Huang, and C. Gan (2016) On mining latent topics from healthcare chat logs. J. Biomed. Inform. 61: 247–259.

    Article  Google Scholar 

  19. He, Q., B. Chen, J. Pei, B. Qiu, P. Mitra, and L. Giles (2009) Detecting topic evolution in scientific literature: how can citations help?. Proceedings of the 18th ACM Conference on Information and Knowledge Management (CIKM ’09). November 2–6. New York, USA.

    Google Scholar 

  20. Newman, D., A. Asuncion, P. Smyth, and M. Welling (2009) Distributed algorithms for topic models. J. Mach. Learn. Res. 10: 1801–1828.

    Google Scholar 

  21. National Science Foundation, Convergence Research at NSF. https://www.nsf.gov/od/oia/convergence/reports.jsp.

  22. ZDNet Korea, MSIT, Investing KRW 37.5 billion in the development of convergence technologies this year. http://www.zdnet.co.kr/view/?no=20190113104730.

  23. Biotech Policy Research Center (2015) 2015 Discovering Future Emerging Biotechnologies–ICT-Converged Biohealth Top 10 Future Emerging Biotechnologies. BioINsay: Vol. 2.

    Google Scholar 

  24. Choi, H., K. Y. Kwahk, and N. Kim (2018) Discovering promising convergence technologies using network analysis of maturity and dependency of technology. J. Intell. Inform. Syst. 24: 101–124.

    Google Scholar 

  25. Jeong, D. H. and H. S. Joo (2018) Discovering interdisciplinary convergence technologies using content analysis technique based on topic modeling. J. Kor. Soc. Inf. Manag. 35: 77–100.

    Google Scholar 

  26. Macklis, R. M. and N. Sharma (2011) Convergence technology in cancer medicine. Expert Rev. Med. Devices. 8: 263–273.

    Article  Google Scholar 

  27. Jung, S. Y., S. Ahn, K. H. Nam, J. P. Lee, and S. J. Lee (2012) In vivo measurements of blood flow in a rat using X-ray imaging technique. Int. J. Cardiovasc. Imaging. 28: 1853–1858.

    Article  Google Scholar 

  28. Lee, H. and H. Zo (2016) R&D allies: how they impact technology convergence in the area of ICT. Proceedings of 2016 International Conference on Information and Communication Technology Convergence (ICTC). October 19–21. Jeju, Korea.

    Google Scholar 

  29. Huang, B. and L. Huang (2015) Patent-based association analysis for technological convergence of IT and BT. Proceedings of 2015 International Conference on Logistics, Informatics and Service Sciences (LISS). July 27–29. Barcelona, Spain.

    Google Scholar 

  30. Jeong, D. H. (2017) Prescriptive analytics system design fusing automatic classification method and intellectual structure analysis method. J. Kor. Soc. Inf. Manag. 34: 33–57.

    Google Scholar 

  31. Poornima, S. and M. Pushpalatha (2016) A journey from big data towards prescriptive analytics. ARPN J. Eng. Appl. Sci. 11: 11465–11474.

    Google Scholar 

  32. Bertsimas, D. and N. Kallus (2015) From predictive to prescriptive analytics. arXiv preprint. arXiv: 1402.5481.

    Google Scholar 

  33. Song, S. K., D. J. Kim, M. Hwang, J. Kim, D. H. Jeong, S. Lee, H. Jung, and W. Sung (2013) Prescriptive analytics system for improving research power. Proceedings of 2013 IEEE 16th International Conference on Computational Science and Engineering. December 3–5. Sydney, Australia.

    Google Scholar 

  34. Biotech Policy Research Center (2017) 2017 Future Emerging Biotechnologies–Top 10 Future Emerging Technologies Leading Biohealth Issues. BioINsay: Vol. 14.

  35. Biotech Policy Research Center (2018) 2018 Future Emerging Biotechnologies–Top 10 Future Emerging Technologies From the Aspects of Core, Red, Green, White Bio. BioINsay: Vol. 27.

  36. Swarup, V., F. I. Hinz, J. E. Rexach, K. I. Noguchi, H. Toyoshiba, A. Oda, K. Hirai, A. Sarkar, N. T. Seyfried, C. Cheng, S. J. Haggarty, International Frontotemporal Dementia Genomics Consortium, M. Grossman, V. M. Van Deerlin, J. Q. Trojanowski, J. J. Lah, A. I. Levey, S. Kondou, and D. H. Geschwind (2019) Identification of evolutionarily conserved gene networks mediating neurodegenerative dementia. Nat. Med. 25: 152–164.

    Article  CAS  Google Scholar 

  37. Raj, T., Y. I. Li, G. Wong, J. Humphrey, M. Wang, S. Ramdhani, Y. C. Wang, B. Ng, I. Gupta, V. Haroutunian, E. E. Schadt, T. Young-Pearse, S. Mostafavi, B. Zhang, P. Sklar, D. A. Bennett, and P. L. De Jager (2018) Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer’s disease susceptibility. Nat. Genet. 50: 1584–1592.

    Article  CAS  Google Scholar 

  38. Forbes, UT Southwestern Medical Center Researchers Discover Alzheimer’s Vaccine, Hope To Test In Humans Soon. https://www.forbes.com/sites/robinseatonjefferson/2018/11/23/ut-researchers-discover-alzheimers-vaccine-hope-to-test-in-humans-soon/#654b50877e8f.

  39. Grantome, http://grantome.com/search?q=tumor+heterogenity.

  40. Grantome, http://grantome.com/search?q=tumor.

  41. Research and Market, Global Healthcare Connected Wearable Devices Market 2019-2024: Medical, Wellness, and Fitness Markets by Device Type, Body Area, Solution Type, and Health Concerns. https://globenewswire.com/news-release/2019/02/20/1738135/0/en/Global-Healthcare-Connected-Wearable-Devices-Market-2019-2024.Medical-Wellness-and-Fitness-Markets-by-Device-Type-Body-Area-Solution-Type-and-Health-Concerns.html.

  42. Kenneth Research, The Global Wireless Sensor Network Marketwas Valued At Usd 28.97 Billion In 2016 And Is Projected To Reach Usd 131.67 Billion By 2025, Growing At A Cagr Of 18.32% From 2017 To 2025. https://www.openpr.com/news/1586498/THE-GLOBAL-WIRELESS-SENSOR-NETWORK-MARKETWAS-VALUED-AT-USD-28-97.BILLION-IN-2016-AND-IS-PROJECTED-TO-REACH-USD-131-67.BILLION-BY-2025-GROWING-AT-A-CAGR-OF-18-32.FROM-2017-TO-2025-TOP-KEY-PLAYERS-ARE-INTEL-CORPORATION-HUAWEI-INVESTMENT-HOLDING-CO-L.html.

  43. IEEE ICC, https://icc2019.ieee-icc.org/authors/call-symposium-papers.

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Acknowledgement

This research was supported by Duksung Women’s University Research Grants 2017 (3000002847).

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Authors and Affiliations

  1. Department of Library and Information Science, Duksung Women’s University, Seoul, 01369, Korea

    Do-Heon Jeong

  2. Department of Biotechnology, Duksung Women’s University, Seoul, 01369, Korea

    Hwang-Soo Joo

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Correspondence to Hwang-Soo Joo.

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Jeong, DH., Joo, HS. Topical Prescriptive Analytics System for Automatic Recommendation of Convergence Technology. Biotechnol Bioproc E 24, 893–906 (2019). https://doi.org/10.1007/s12257-019-0305-1

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