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

Souad Moufok, Anas Mouattah and Khalid Hachemi

International Journal of Data Mining, Modelling and Management, 2024, vol. 16, issue 1, 49-65

Abstract: 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.

Keywords: look-alike sound-alike; LASA; data mining; medication errors; dispensing errors; k-means; DBSCAN. (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=136215 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdmmm:v:16:y:2024:i:1:p:49-65

Access Statistics for this article

More articles in International Journal of Data Mining, Modelling and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().