SURVEY ON VARIOUS PREDICATION MODELS FOR SIDE EFFECTS IN THE DRUG TO DRUG INTERACTIONS USING MACHINE LEARNING MODELS

In this survey, we present various models on drug interactions and its side effects on humans via several literatures. It is safe to say that in the pharmaceutical sector, there is no more important clinical trial than one designed to identify drug side effects. This is because no drug has ever been...

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Bibliographic Details
Published inNeuroQuantology Vol. 20; no. 15; p. 4668
Main Authors Arunkumar, M, Baskaran, T S
Format Journal Article
LanguageEnglish
Published Bornova Izmir NeuroQuantology 01.01.2022
Subjects
Algorithms
Bias
Computer science
Diabetes
Drug interactions
Heart attacks
Machine learning
Methods
Pharmaceutical industry
Pharmacists
Prediction models
Prescription drugs
Sample variance
Side effects
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ISSN1303-5150
DOI10.14704/NQ.2022.20.15.NQ88473

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Summary:In this survey, we present various models on drug interactions and its side effects on humans via several literatures. It is safe to say that in the pharmaceutical sector, there is no more important clinical trial than one designed to identify drug side effects. This is because no drug has ever been the subject of a randomized controlled trial designed to discover its possible beneficial benefits. The chemical and biological make-up of the drugs is where the majority of current research interest goes when trying to predict unfavorable pharmacological reactions. The results of a survey of different prediction models for spotting side effects caused by drug interactions in the human body are presented in this article. Predicting the outcomes of medication interactions was the focus of this survey.
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ISSN:1303-5150
DOI:10.14704/NQ.2022.20.15.NQ88473