K-means and DBSCAN for look-alike sound-alike medicines issue

The goal of this study is to analyse the application of data mining techniques in clustering drug names based on their spelling similarity in order to reduce the occurrence of dispensing errors caused by look-alike sound-alike medicine confusion, as they considered one of the most common causes of dispensing errors. Two unsupervised data mining methods, k-means and DBSCAN, were used in conjunction with two similarity measures, BiSim and Levenshtein. The results of the study showed that the approach is effective in identifying potential confusable medicines, with BiSim-based k-means clustering being favoured with a silhouette score of 0.5.