The rise of polypharmacy requires from health providers an#R##N#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#R##N#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#R##N#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 77%. Similarity to a drug the patient#R##N#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 work ow, will reduce prescription errors#R##N#thereby increasing the health outcome of the patient.
['Wee Goh', 'D. Tao', 'Ji Zhang', 'Jianming Yong']
Mining Drug Properties for Decision Support in Dental Clinics.