Exploring the Precision Medicine Research Trend using Topic Modeling

January 31, 2024  |  Vol.10, No.1  |  PP. 387-398  | PDF

AUTHORS:

Su Hyun Ahn, College of General Education, Semyung University, South Korea

Jun Hyeok Ko, Department of Business Administration, Semyung University, South Korea

Sang Jun Lee, College of General Education, Semyung University, South Korea

KEYWORDS:

Precision Medicine, Text Mining, Topic Modeling, Research Trend

Abstract

Precision medicine is a technology that provides optimal and customized healthcare services by analyzing an individual's genetic information and life information. This study aims to investigate the current research trends in precision medicine and provide recommendations for the field's future direction. Therefore, after data collection and preprocessing, major keyword analysis and topic modeling were conducted on domestic media reports. As a result of the analysis, Topic 1 is "Industrial innovation and technology-driven job creation", Topic 2 is "Innovation of precision medical services and utilization of big data", Topic 3 is "Advancement of precision medicine through research and innovation", Topic 4 is "Precision medical data management using big data and cloud", and Topic 5 is "Advancement of genomic and biological research", Topic 6 is "Business and innovation through biohealth and convergence technologies," Topic 7 is "Competition and collaboration in the global marketplace," Topic 8 is "Collaboration between hospitals centered on precision medicine and data technologies," and Topic 9 is "Research and development for drug discovery and personalized care." The findings are expected to provide new insights into various fields by identifying key topics and trends in precision medicine.

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References:

[1] Jun-hyun Song, Il-Gon, Kim Sun-Ju Ahn, Trends in Precision Medical Data Standardization, TTA Journal, (2017), Vol.172, pp.86-91.
Available from: https://scienceon.kisti.re.kr/commons/util/originalView.do?cn=JAKO201764656419314&oCn=
JAKO201764656419314&dbt=JAKO&journal=NJOU00292067
[2] Hye-Sun Yoon, Precision Medicine in Regulatory Perspectives: Triggered by the American Precision Medicine Initiative, Hanyang Law Review, (2018), Vol.35, No.4, pp.55-91.
DOI: 10.18018/HYLR.2018.35.4.055
[3] Je-su Shin, Sang-ho Bae, Nam-yong Lee, Jin-ho Park, An Empirical Study of Healthcare & Medicine Information Protection Model for Precision Medicine Initiative, Jounal of The Korea Society of Information Technology Policy & Management, (2016), Vol.8, No.4, pp.209-217.
UCI: G704-SER000003986.2016.8.4.001
[4] Kyu-pyo Kim, Applying Precision Medicine in Clinical Practice, The Korean Journal of Medicine, (2020), Vol.95, No.6, pp.382-386.
DOI: 10.3904/kjm.2020.95.6.382
[5] Se-young Moon, Gi-jeong Jang, Han-hae Kim, A Strategy of Success in Precision Medicine, KISTEP, (2016)
Available from: http://www.kistep.re.kr/c3/sub3_2.jsp?brdType=R&bbIdx=10502
[6] Gyu-pyo Kim, Applying Precision Medicine in Clinical Practice, The Korean Journal of Medicine, (2020), Vol.95, No.6, pp.382-386.
DOI: 10.3904/kjm.2020.95.6.382
[7] D. M. Blei, Probabilistic Topic Models, Communications of the ACM, (2012), Vol.55, No.4, pp.77-84.
DOI: 10.1145/2133806.2133826
[8] A. P. Shiryaev, A. V. Dorofeev, A. R. Fedorov, L. G. Gagarina, V. V. Zaycev, LDA Models for Finding Trends in Technical Knowledge Domain, In 2017 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), (2017)
DOI: 10.1109/EIConRus.2017.7910614
[9] L. Sun, Y. Yin, Discovering Themes and Trends in Transportation Research using Topic Modeling, Transportation Research Part C: Emerging Technologies, (2017), Vol.77, pp.49-66.
DOI: 10.1016/j.trc.2017.01.013
[10] L. Liu, L. Tang, W. Dong, S. Yao, W. Zhou, An Overview of Topic Modeling and Its Current Applications in Bioinformatics, SpringerPlus, (2016), Vol.5, No.1, pp.1-22.
DOI: 10.1186/s40064-016-3252-8
[11] D. Hall, D. Jurafsky, C. D. Manning, Studying the History of Ideas using Topic Models, In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, (2008)
Available from: https://aclanthology.org/D08-1038
[12] https://www.bigkinds.or.kr, Jun 30 (2023)
[13] Yong-min Baek, Text-Mining using R, Hanulm, (2020)
[14] Min Song, Text Mining, Chongram, (2017)
[15] D. M. Blei, Y. N. Andrew, I. J. Michael, Latent Dirichlet Allocation, Journal of Machine Learning Research, (2003), Vol.3, pp.993-1022.
[16] Young-woo Kim, Do It! R Text Mining, EasyPublising, (2021)
[17] T. L. Griffiths, M. Steyvers, Finding Scientific Topics, Proceedings of the National academy of Sciences, (2004), Vol.101, pp.5228-5235.
DOI: 10.1073/pnas.0307752101

Citations:

APA:
Ahn, S. H., Ko, J. H., Lee, S. J. (2024). Exploring the Precision Medicine Research Trend using Topic Modeling. Asia-pacific Journal of Convergent Research Interchange (APJCRI), ISSN: 2508-9080 (Print); 2671-5325 (Online), KCTRS, 10(1), 387-398. doi: 10.47116/apjcri.2024.01.30.

MLA:
Ahn, Su Hyun, et al. “Exploring the Precision Medicine Research Trend using Topic Modeling.” Asia-pacific Journal of Convergent Research Interchange, ISSN: 2508-9080 (Print); 2671-5325 (Online), KCTRS, vol. 10, no. 1, 2024, pp. 387-398. APJCRI, http://apjcriweb.org/content/vol10no1/30.html.

IEEE:
[1] S. H. Ahn, J. H. Ko, S. J. Lee, “Exploring the Precision Medicine Research Trend using Topic Modeling.” Asia-pacific Journal of Convergent Research Interchange (APJCRI), ISSN: 2508-9080 (Print); 2671-5325 (Online), KCTRS, vol. 10, no. 1, pp. 387-398, January 2024.


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