Knowledge graph-based convolutional network coupled with sentiment analysis towards enhanced drug recommendation

Document Type

Article

Source of Publication

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Publication Date

11-28-2022

Abstract

Recommending appropriate drugs to patients based on their history and symptoms is a complex real-world problem. Knowing whether a drug is useful without its consumption by a variety of people followed by proper evaluation is a challenge. Modern-day recommender systems can assist in this provided they receive large data to learn. Public reviews on various drugs are available for knowledge sharing. These reviews assist in recommending the best and most appropriate option to the user. The explicit feedback underpins the entire recommender system. This work develops a novel knowledge graph-based convolutional network for recommending drugs. The knowledge graph is coupled with sentiment analysis extracted from the public reviews on drugs to enhance drug recommendations. For each drug that has been used previously, sentiments have been analyzed to determine which one has the most effective reviews. The knowledge graph effectively captures user-item relatedness by mining its associated attributes. Experiments are performed on public benchmarks and a comparison is made with closely related state-of-the-art works. Based on the obtained results, the current work performs better than the past contributions by achieving up to 98.7% Area Under Curve (AUC) score.

ISSN

1545-9964

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Volume

PP

Issue

99

First Page

1

Last Page

12

Disciplines

Computer Sciences

Keywords

Drugs, Recommender systems, Support vector machines, Sentiment analysis, Knowledge engineering, Motion pictures, Radio frequency

Indexed in Scopus

no

Open Access

no

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