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- W4328005955 abstract "The medical social platforms enable users to exchange their reviews that reflect their experience over the drugs. The classification of patients' reviews on drugs into positive or negative ratings and the discovery of the drugs that provoke an Adverse Drug Reaction (ADR) can guide the medical sector and the pharmaceutical sector to understand the patients' discomfort or the patients' satisfaction with specific drugs. This paper presents a model to predict the rating of drugs based on review analysis using natural language processing techniques and machine learning algorithms. The model enables drugs discovery which provokes ADRs via the sentiment extraction of the reviews using the syuzhet package in R-program. The proposed model was applied to the Drugs.com dataset and evaluated using another dataset (drugs.lib) that is not used during the training of the machine learning algorithms. The proposed model achieves an accuracy of 87% and 78% for the Drugs.com and DrugsLib.com testing. Furthermore, the model was applied on Drugs.com dataset to discover the ADRs. It classifies the side effects grading of each review based on its sentiment analysis result. The proposed model discovers 44534 patient reviews out of 213865 indicate that the patients suffer from moderate side effects. Also, it discovers 66 reviews out of 213865 indicate that the patients suffer from severe side effects. Both of the moderate and severe side effects are considered as a trigger of ADRs occurrence. In other words, 21.03% of drugs reviews indicate that the patient suffer from ADRs due the drug consumption." @default.
- W4328005955 created "2023-03-22" @default.
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- W4328005955 date "2022-08-01" @default.
- W4328005955 modified "2023-09-23" @default.
- W4328005955 title "A Proposed Model for Drugs' Review Analysis and Adverse Drug Reaction Discovery" @default.
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- W4328005955 doi "https://doi.org/10.1109/mcsi55933.2022.00028" @default.
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