Personalised Drug Prescription for Dental Clinics Using Word Embedding

Abstract

The number of drugs in drug databases is constantly expanding with novel drugs appearing on the market each year. A dentist cannot be expected to recall all the drugs available, let alone potential drug-drug interactions (DDI). This can be problematic when dispensing drugs to patients especially those with multiple medical conditions who often take a multiple medications. Any new medication prescribed must be checked against the patient's medical history, in order to avoid drug allergies and reduce the risk of adverse reactions. Current drug databases allowing the dentist to check for DDI are limited by the lack of integration of the patient's medical profile with the drug to be prescribed. Hence, this paper introduces a software which predicts the possible DDI of a new medication against the patient's medical profile, based on previous findings that associate similarity ratio with DDI. This system is based conceptually on a three-tier framework consisting of a knowledge layer, prediction layer and presentation layer. The novel approach of this system in applying feature vectors for drug prescription will be demonstrated during the conference (http://r.glory.sg/main.php). By engaging with the interactive demonstration, participants will gain first-hand experience in the process from research idea to implementation. Future work includes the extension of use from dental to medical institutions, and it is currently being enhanced to serve as a training tool for medical students.