Drug prescription support in dental clinics through drug corpus mining

Wee Pheng GOH, Xiaohui TAO, Ji ZHANG, Jianming YONG, Wenping ZHANG, Haoran XIE

Research output: Contribution to journalArticlespeer-review

2 Citations (Scopus)


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. Copyright © 2018 Springer Nature Switzerland AG.
Original languageEnglish
Pages (from-to)341-349
JournalInternational Journal of Data Science and Analytics
Issue number4
Early online date18 Aug 2018
Publication statusPublished - Dec 2018


Goh, W. P., Tao, X., Zhang, J., Yong, J., Zhang, W., & Xie, H. (2018). Drug prescription support in dental clinics through drug corpus mining. International Journal of Data Science and Analytics, 6(4), 341-349. doi: 10.1007/s41060-018-0149-3


  • Adverse relationship
  • Word embeddings
  • Term similarity
  • Personalised prescription
  • Drug properties


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