Skip to main content

Advertisement

SpringerLink
Log in

Drug prescription support in dental clinics through drug corpus mining

  • Regular Paper
  • Published:
International Journal of Data Science and Analytics Aims and scope Submit manuscript

Abstract

The rapid increase in the volume and variety of data poses a challenge to safe drug prescription for the dentist. The increasing number of patients that take multiple drugs further exerts pressure on the dentist to make the right decision at point-of-care. Hence, a robust decision support system will enable dentists to make decisions on drug prescription quickly and accurately. Based on the assumption that similar drug pairs have a higher similarity ratio, this paper suggests an innovative approach to obtain the similarity ratio between the drug that the dentist is going to prescribe and the drug that the patient is currently taking. We conducted experiments to obtain the similarity ratios of both positive and negative drug pairs, by using feature vectors generated from term similarities and word embeddings of biomedical text corpus. This model can be easily adapted and implemented for use in a dental clinic to assist the dentist in deciding if a drug is suitable for prescription, taking into consideration the medical profile of the patients. Experimental evaluation of our model’s association of the similarity ratio between two drugs yielded a superior F score of 89%. Hence, such an approach, when integrated within the clinical work flow, will reduce prescription errors and thereby increase the health outcomes of patients.

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.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. http://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm212844.htm.

References

  1. Lieber, N.S.R., Ribeiro, E.: Adverse drug reactions leading children to the emergency department. Rev. Bras. Epidemiol. 15, 265–274 (2012)

    Article  Google Scholar 

  2. Brown, A.S., Patel, C.J.: Meshdd: literature-based drug-drug similarity for drug repositioning. J. Am. Med. Inf. Assoc. 27, 1–5 (2016)

    Google Scholar 

  3. Bui, Q., Sloot, P., van Mulligen, E., Kors, J.: A novel feature-based approach to extract drug-drug interactions from biomedical text. BioInformatics 30(23), 3365–3371 (2014)

    Article  Google Scholar 

  4. Casillas, A., Pérez, A., Oronoz, M., Gojenola, K., Santiso, S.: Learning to extract adverse drug reaction events from electronic health records in spanish. Expert Syst. Appl. 61, 235–245 (2016)

    Article  Google Scholar 

  5. Goh, W.P., Tao, X., Zhang, J., Yong, J.: Mining drug properties for decision support in dental clinics. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017, Part 2, pp. 375–387. Springer, Berlin (2017)

    Google Scholar 

  6. Wu, H.-Y., Chiang, C.-W., Li, L.: Text mining for drug–drug interaction. Methods Mol. Biol. 1159, 47–75 (2014)

    Article  Google Scholar 

  7. Bokharaeian, B., Diaz, A., Chitsaz, H.: Enhancing extraction of drug-drug interaction from literature using neutral candidates, negation, and clause dependency. PLoS ONE 11(10), 1–20 (2016)

    Article  Google Scholar 

  8. Li, A., Zang, Q., Sun, D., Wang, M.: A text feature-based approach for literature mining of lncrna-protein interactions. Neurocomput. 206(C), 73–80 (2016). https://doi.org/10.1016/j.neucom.2015.11.110

    Article  Google Scholar 

  9. Tari, L., Anwar, S., Liang, S., Baral, J.C.: Discovering drug-drug interactions a text-mining and reasoning approach based on properties of drug metabolism. Bioinformatics 26(18), 547–553 (2010)

    Article  Google Scholar 

  10. Yan, S., Jiang, X., Chen, Y.: Text mining driven drug–drug interaction detection. In: 2013 IEEE International Conference on Bioinformatics and Biomedicine, pp. 349–354 (2013)

  11. Goh, W.P., Tao, X., Zhang, J., Yong, J.: Decision support systems for adoption in dental clinics: a survey. Knowl. Based Syst. 104, 195–206 (2016)

    Article  Google Scholar 

  12. Park, S.G., Lee, S., Kim, M.-K., Kim, H.-G.: Shared decision support system on dental restoration. Expert Syst. Appl. 39(14), 11775–11781 (2012)

    Article  Google Scholar 

  13. Smart, P.R., Sadraie, M.: Applications and uses of dental ontologies. In: Proceedings of the 2012 IADIS International Conference, pp. 499–504 (2012)

  14. Bhatia, A., Singh, R.: Using bayesian network as decision making system tool for deciding treatment plan for dental caries. J. Acad. Ind. Res. 2(2), 93–96 (2013)

    Google Scholar 

  15. Bessani, M., Lins, E., Delbem, A., Maciel, C.: Construction of a clinical decision support system for dental caries management using BN. In: Brazilian Congress on Biomedical Engineering, pp. 517–520 (2014)

  16. Dechanont, S., Maphanta, S., Butthum, B., Kongkaew, C.: Hospital admissions/visits associated with drug-drug interactions: a systematic review and meta-analysis. Pharmacoepidemiol. Drug Saf. 23(5), 489–497 (2014)

    Article  Google Scholar 

  17. Cai, Y., Au Yeung, C.-m., Leung, H.-f.: Knowledge representation on the web. In: Fuzzy Computational Ontologies in Contexts: Formal Models of Knowledge Representation with Membership Degree and Typicality of Objects, and Their Applications, pp. 15–21. Springer, Berlin (2012)

    Chapter  Google Scholar 

  18. Ayvaz, S., Horn, J., Hassanzadeh, O., Zhu, Q., Stan, J., Tatonetti, N.P., Vilar, S., Brochhausen, M., Samwald, M., Rastegar-Mojarad, M., Dumontier, M., Boyce, R.D.: Toward a complete dataset of drug-drug interaction information from publicly available sources. Biomed. Inf. 55, 206–217 (2015)

    Article  Google Scholar 

  19. Sen, S., Swoap, A.B., Li, Q., Boatman, B., Dippenaar, I., Gold, R., Ngo, M., Pujol, S., Jackson, B., Hecht, B.: Cartograph: unlocking spatial visualization through semantic enhancement. In: Proceedings of the 22nd International Conference on Intelligent User Interfaces, IUI’17, pp. 179–190. ACM, New York (2017)

  20. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR arXiv:1301.3781

  21. Drozd, A., Gladkova, A., Matsuoka, S.: Word embeddings, analogies, and machine learning: Beyond king – man + woman = queen. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics, pp. 3519–3530 (2016)

  22. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Burges, C.J.C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 26, pp. 3111–3119. Curran Associates Inc., Red Hook (2013)

    Google Scholar 

  23. Zhang, Y., Jatowt, A., Tanaka, K.: Towards understanding word embeddings: automatically explaining similarity of terms. In: 2016 IEEE International Conference on Big Data (Big Data), pp. 823–832 (2016)

Download references

Acknowledgements

This research is partially supported by Glory Dental Surgery (Roxy Square) Pte Ltd, Singapore (http://glory.sg), and undertaken collaboratively with their panel of dentists. The authors would like to thank the reviewers, the handling editor and the Editor-in-Chief for their constructive comments on the manuscript. We would also like to thank Dr Elizabeth Goh (e-mail: eg@glory.sg) for enriching the authors’ understanding of dentists’ requirements and for proof-reading the manuscript.

Author information

Authors and Affiliations

  1. Faculty of Health, Engineering and Sciences, University of Southern Queensland, Toowoomba, Australia

    Wee Pheng Goh, Xiaohui Tao & Ji Zhang

  2. Faculty of Business, Education, Law and Arts, University of Southern Queensland, Toowoomba, Australia

    Jianming Yong

  3. School of Information, Renmin University, Beijing, China

    Wenping Zhang

  4. Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR, China

    Haoran Xie

Authors
  1. Wee Pheng Goh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaohui Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ji Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jianming Yong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wenping Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Haoran Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Wee Pheng Goh or Xiaohui Tao.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

This paper is an extension version of the PAKDD 2017 Long Presentation paper “Mining Drug Properties for Decision Support in Dental Clinics”.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Goh, W.P., Tao, X., Zhang, J. et al. Drug prescription support in dental clinics through drug corpus mining. Int J Data Sci Anal 6, 341–349 (2018). https://doi.org/10.1007/s41060-018-0149-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41060-018-0149-3

Keywords

Search

Navigation