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.
|Number of pages||9|
|Journal||International Journal of Data Science and Analytics|
|Early online date||18 Aug 2018|
|Publication status||Published - Dec 2018|
Bibliographical noteThis paper is an extension version of the PAKDD 2017 Long Presentation paper “Mining Drug Properties for Decision Support in Dental Clinics”.
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.
- Adverse relationship
- Word embeddings
- Term similarity
- Personalised prescription
- Drug properties
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. https://doi.org/10.1007/s41060-018-0149-3