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Data Analytics in the Pharmacology Domain

Data Analytics in the Pharmacology Domain

Maryam Qusay Yousif Helae, Dariush Ebrahimi, Fadi Alzhouri
Copyright: © 2022 |Volume: 7 |Issue: 1 |Pages: 16
ISSN: 2379-738X|EISSN: 2379-7371|EISBN13: 9781683182986|DOI: 10.4018/ijbdah.314229
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MLA

Helae, Maryam Qusay Yousif, et al. "Data Analytics in the Pharmacology Domain." IJBDAH vol.7, no.1 2022: pp.1-16. http://doi.org/10.4018/ijbdah.314229

APA

Helae, M. Q., Ebrahimi, D., & Alzhouri, F. (2022). Data Analytics in the Pharmacology Domain. International Journal of Big Data and Analytics in Healthcare (IJBDAH), 7(1), 1-16. http://doi.org/10.4018/ijbdah.314229

Chicago

Helae, Maryam Qusay Yousif, Dariush Ebrahimi, and Fadi Alzhouri. "Data Analytics in the Pharmacology Domain," International Journal of Big Data and Analytics in Healthcare (IJBDAH) 7, no.1: 1-16. http://doi.org/10.4018/ijbdah.314229

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Abstract

Data mining approaches such as natural language processing play a fundamental role in the healthcare sector and, exclusively, the pharmacology domain. The substantial feedback and experiences shared by the patients on different drugs are employed to perform opinion mining on the reviews, which will help the decision-makers to improve the medications' quality and provide the optimal medical outcomes. Based on that, the drug review data set from the UCI machine learning repository is used. The objective of this study is to conduct a sentiment analysis of the patients' reviews to obtain their satisfaction with different drugs using the random forest (RF) machine learning model. In addition, finding out the best drugs for different conditions based on patients' reviews is done by implementing the long short-term memory. Finally, the authors predict the patients' medical conditions based on their reviews by performing the support vector machine and RF classifiers. The knowledge of the patients' medical condition and satisfaction will lead to a noticeable improvement in the pharmaceutical and medical consequences.