Mining Drug Properties for Decision Support in Dental Clinics

  • Wee Pheng Goh
  • Xiaohui Tao
  • Ji Zhang
  • Jianming Yong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10235)


The rise of polypharmacy requires from health providers an awareness of a patient’s drug profile before prescribing. Existing methods to extract information on drug interactions do not integrate with the patient’s medical history. This paper describes state-of-the-art approaches in extracting the term frequencies of drug properties and combining this knowledge with consideration of the patient’s drug allergies and current medications to decide if a drug is suitable for prescription. Experimental evaluation of our models association of the similarity ratio between two drugs (based on each drug’s term frequencies) with the similarity between them yields a superior accuracy of 79%. Similarity to a drug the patient is allergic to or is currently taking are important considerations as to the suitability of a drug for prescription. Hence, such an approach, when integrated within the clinical workflow, will reduce prescription errors thereby increasing the health outcome of the patient.


Adverse relationship Drug allergy Drug properties Knowledge-base Personalised prescription Similarity ratio Term frequency 



This research is partially supported by Glory Dental Surgery Pte Ltd, Singapore ( and undertaken collaboratively with their panel of dentists. We would like to thank Ms Elizabeth Goh for enriching the authors’ understanding of dentists’ requirements.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Wee Pheng Goh
    • 1
  • Xiaohui Tao
    • 1
  • Ji Zhang
    • 1
  • Jianming Yong
    • 2
  1. 1.Faculty of Health, Engineering and SciencesUniversity of Southern QueenslandToowoombaAustralia
  2. 2.Faculty of Business, Education, Law and ArtsUniversity of Southern QueenslandToowoombaAustralia

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