Title: Recommending alternative drugs by using generic drug names to minimise side effects

Authors: Sohee Hwang; Jungrim Kim; Jeongwoo Kim; Sanghyun Park

Addresses: Department of Computer Science, Yonsei University, Seoul, Korea ' Department of Computer Science, Yonsei University, Seoul, Korea ' Department of Computer Science, Yonsei University, Seoul, Korea ' Department of Computer Science, Yonsei University, Seoul, Korea

Abstract: Healthcare and the treatment of illnesses are one of the most fundamental aspects of modern human life, and drugs are the easiest approach to healthcare. However, consuming drugs lead to diverse effects. We propose the use generic medicine names and it is important to note that while drugs with the same generic name serve similar purposes, they may also cause different side effects. This paper presents a strategy to address the issue of side effects by recommending alternative drugs that have the same therapeutic effect but with less detrimental effects. By integrating the generic names of drugs and data from social networks, more data can be obtained to arrive at meaningful conclusions. This paper proposes a new approach for analysing drug-induced side effects, with collecting, processing, and using data from social networks.

Keywords: data mining; drug recommendation; adverse drug reaction; social data; side effect; generic name; drug-induced; user comment; alleviated side effect; alternative drug.

DOI: 10.1504/IJDMB.2017.088139

International Journal of Data Mining and Bioinformatics, 2017 Vol.18 No.4, pp.301 - 314

Received: 29 Jun 2017
Accepted: 26 Jul 2017

Published online: 21 Nov 2017 *

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