Mohanapriya, D and Beena, R (2020) Survey on Pharmacovigilance System for Predicting Drug Indications and Side Effects. Survey on Pharmacovigilance System for Predicting Drug Indications and Side Effects, 5 (5). pp. 1081-1086. ISSN 2456-2165

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Abstract

Drugs are chemicals that treat, prevent or
diagnosis diseases which is also known as medicine or
medication. The task of discovering a new drug for a
specific disease requires high cost and long time, instead
the prediction of remedial and side effects of available
drugs preciously supports to recommend candidate
drugs for specific diseases with low cost and time. The
recommended drugs for diseases are predicted by
analyzing relationship between the drugs-side effects,
drugs-genes and drugs-diseases. The extraction of terms
which are related to drug, genes, disease and side effects
from textual documents, literatures and biomedical
repositories are used to mine relationship among them.
Many text mining based drug recommendation
approaches were proposed in the literature. The drug
indication and side effects were mined mostly based on
topic modeling, machine learning, Feature dependency
graph and Similarity based approaches. This article
presents a detailed survey of predicting drug indications
and side effects using text mining approaches. At first,
different approaches are analyzed in depth, established
from previous research. Furthermore, a comparative
study is performed to identify the limitations of current
methods and provide a suggestion for further progress
in the estimation of signs of drugs and side effectrepositioning. The importance of drug repositioning has
grown dramatically due to the massive increase in the cost
of new drug production [1]. So it reduces the drug
development cost and time. Various methods have been
proposed for drug repositioning based on computational
methods was proposed due to the exponential increase in
available genomic / phenotypic data and the appearance of
various methods for data analysis, such as machine learning,
text mining [2]. Text mining is a technique of data mining
and is used to extract meaningful knowledge from huge
unstructured text data. Text mining is more successful as it
leverages secondary data resources to help meet the goal of
detecting side effects as soon as proper drug advice
contributes to enhanced drug safety is accurate. Various
techniques were implemented using this method. By using
this method, various techniques were performed. In this
study, various techniques are investigated for predicting
drug indication and its side effects.

Item Type: Article
Uncontrolled Keywords: Drug discovery, Drug indication, Side effects, Natural language processing, Text mining.
Divisions: PSG College of Arts and Science > Department of Computer Science
Depositing User: Mr Team Mosys
Date Deposited: 29 Feb 2024 11:18
Last Modified: 29 Feb 2024 11:18
URI: http://ir.psgcas.ac.in/id/eprint/2116

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