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Exploring new digital therapeutics technologies for psychiatric disorders using BERTopic and PatentSBERTa

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  • Jeon, Eunji
  • Yoon, Naeun
  • Sohn, So Young

Abstract

Coronavirus disease 2019 (COVID-19) has accelerated the growth of the digital therapeutics (DTx) market; therefore, development strategies for new DTx products are necessary to satisfy market needs. However, data-driven methods for recommending digital healthcare technologies for novel DTx applications are scarce. We propose a technology opportunity discovery framework that recommends 1) potential technologies as new DTx products, and 2) the applicable target disorders. We applied BERTopic and PatentSBERTa to patents filed with the United States Patent and Trademark Office and calculated the score of potential technologies on the basis of their thematic characteristics with respect to their digital capabilities and similarity to DTx technologies. By identifying the target disorder of similar technologies, specific disorders were proposed that can be treated with the proposed technique. By applying the proposed framework to psychiatric disorders—one of the largest therapeutic areas of DTx, we recommend digital monitoring technologies applicable to poor breathing or sleeping patterns for cognitive impairment. Furthermore, we provide strategies to utilize the recommended digital technologies for DTx for specific disorders to facilitate a direct intervention or treatment, which can contribute to the planning of roadmaps for DTx.

Suggested Citation

  • Jeon, Eunji & Yoon, Naeun & Sohn, So Young, 2023. "Exploring new digital therapeutics technologies for psychiatric disorders using BERTopic and PatentSBERTa," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
  • Handle: RePEc:eee:tefoso:v:186:y:2023:i:pa:s0040162522006515
    DOI: 10.1016/j.techfore.2022.122130
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    as
    1. Yoon, Janghyeok & Park, Hyunseok & Seo, Wonchul & Lee, Jae-Min & Coh, Byoung-youl & Kim, Jonghwa, 2015. "Technology opportunity discovery (TOD) from existing technologies and products: A function-based TOD framework," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 153-167.
    2. Vesselkov, Alexandr & Hämmäinen, Heikki & Töyli, Juuso, 2018. "Technology and value network evolution in telehealth," Technological Forecasting and Social Change, Elsevier, vol. 134(C), pages 207-222.
    3. Andrew Rodriguez & Byunghoon Kim & Mehmet Turkoz & Jae-Min Lee & Byoung-Youl Coh & Myong K. Jeong, 2015. "New multi-stage similarity measure for calculation of pairwise patent similarity in a patent citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 565-581, May.
    4. Gianluca Tarasconi & Carlo Menon, 2017. "Matching Crunchbase with patent data," OECD Science, Technology and Industry Working Papers 2017/07, OECD Publishing.
    5. Karvonen, Matti & Kässi, Tuomo, 2013. "Patent citations as a tool for analysing the early stages of convergence," Technological Forecasting and Social Change, Elsevier, vol. 80(6), pages 1094-1107.
    6. Kim, Tae San & Sohn, So Young, 2020. "Machine-learning-based deep semantic analysis approach for forecasting new technology convergence," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    7. Park, Youngjin & Yoon, Janghyeok, 2017. "Application technology opportunity discovery from technology portfolios: Use of patent classification and collaborative filtering," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 170-183.
    8. Yonghan Ju & So Young Sohn, 2015. "Identifying patterns in rare earth element patents based on text and data mining," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 389-410, January.
    9. Geiger, Susi & Kjellberg, Hans, 2021. "Market mash ups: The process of combinatorial market innovation," Journal of Business Research, Elsevier, vol. 124(C), pages 445-457.
    10. Jae Ha Gwak & So Young Sohn, 2018. "A novel approach to explore patent development paths for subfield technologies," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(3), pages 410-419, March.
    11. Venugopalan, Subhashini & Rai, Varun, 2015. "Topic based classification and pattern identification in patents," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 236-250.
    12. Jee, Su Jung & Kwon, Minji & Ha, Jung Moon & Sohn, So Young, 2019. "Exploring the forward citation patterns of patents based on the evolution of technology fields," Journal of Informetrics, Elsevier, vol. 13(4).
    13. Hamid Bekamiri & Daniel S. Hain & Roman Jurowetzki, 2021. "PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT," Papers 2103.11933, arXiv.org, revised Oct 2021.
    14. Choi, Jaewoong & Jeong, Byeongki & Yoon, Janghyeok, 2019. "Technology opportunity discovery under the dynamic change of focus technology fields: Application of sequential pattern mining to patent classifications," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    15. David Lenz & Peter Winker, 2020. "Measuring the diffusion of innovations with paragraph vector topic models," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-18, January.
    16. Lee, MyoungHoon & Kim, Suhyeon & Kim, Hangyeol & Lee, Junghye, 2022. "Technology Opportunity Discovery using Deep Learning-based Text Mining and a Knowledge Graph," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    17. Aaldering, Lukas Jan & Song, Chie Hoon, 2021. "Of leaders and laggards - Towards digitalization of the process industries," Technovation, Elsevier, vol. 105(C).
    18. Shaobo Li & Jie Hu & Yuxin Cui & Jianjun Hu, 2018. "DeepPatent: patent classification with convolutional neural networks and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 721-744, November.
    19. Song, Bomi & Suh, Yongyoon, 2019. "Identifying convergence fields and technologies for industrial safety: LDA-based network analysis," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 115-126.
    20. Lee, Jiho & Ko, Namuk & Yoon, Janghyeok & Son, Changho, 2021. "An approach for discovering firm-specific technology opportunities: Application of link prediction to F-term networks," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    21. Ma, Tingting & Zhou, Xiao & Liu, Jia & Lou, Zhenkai & Hua, Zhaoting & Wang, Ruitao, 2021. "Combining topic modeling and SAO semantic analysis to identify technological opportunities of emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    22. Luciano Kay & Nils Newman & Jan Youtie & Alan L. Porter & Ismael Rafols, 2014. "Patent overlay mapping: Visualizing technological distance," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(12), pages 2432-2443, December.
    23. Sunhye Kim & Inchae Park & Byungun Yoon, 2020. "SAO2Vec: Development of an algorithm for embedding the subject–action–object (SAO) structure using Doc2Vec," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-26, February.
    24. Tadeusz A. Grzeszczyk & Michal K. Grzeszczyk, 2021. "Improving the Discovery of Technological Opportunities Using Patent Classification Based on Explainable Neural Networks," European Research Studies Journal, European Research Studies Journal, vol. 0(3), pages 402-409.
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    2. Qiao Lin & Zhulin Xin & Shuang Peng & Ruixue Zhao & Yingli Nie & Youtao Chen & Xuebin Yin & Guojian Xian & Qiang Zhang, 2024. "Research on Topic Mining and Evolution Trends of Functional Agriculture Based on the BERTopic Model," Agriculture, MDPI, vol. 14(10), pages 1-22, September.

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