Mining Drug Properties for Decision Support in Dental Clinics

With poly-pharmacy becoming more common, it is important for health providers to be aware of the drug profile of patients before prescribing. Although there are many methods on extracting information on drug interactions, they do not integrate with the patients’ medical history. This paper describes state-of-the art approaches in extracting the term frequencies of drug properties, and using this knowledge to decide if a drug is suitable for prescription after considering if there is any drug allergy the patient may have and the drugs that the patient is currently taking. An experiment is conducted to evaluate the accuracy of associating the similarity ratio in terms of their term frequencies to the similarity between them. Experimental evaluation of our model yields an accuracy of over 80% which is superior to models that use other methods. Since a drug is to be avoided if it is similar to a drug that patient is allergic to, our model will help dentist decide if a drug is suitable for prescription to the patient. Hence such an approach, when integrated within the clinical workflow will reduce prescription errors thereby increasing the health outcome of the patients.