Drug prescription support in dental clinics through drug corpus mining

Abstract

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 bio-medical 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.

Introduction to the special issue on Data Science in Asia (with PAKDD’2017)

“…• Drug prescription support in dental clinics through drug corpus mining [6] In this paper, the authors suggested an 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. • Inferring variable labels using outlines of data in Data Jackets by considering similarity and co-occurrence [7] In this paper, the authors focused on similarity among the outlines of Data Jackets (DJs) and presented two models for inferring variable labels (VLs) based on the similarity and the co-occurrence of the VLs.…”

CBOWRA: A Representation Learning Approach for Medication Anomaly Detection

“…The situation is similar to that in medication fraud detection, in which we cannot simply treat mismatches as anomalies. In the same time, mining the context of drug corpus has drawn attentions in recent years [17], which motivates us to detect the errors through mining the semantic relationship between diagnoses and prescriptions.…”