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Research Article

Drug Recommendations Based On Aspect Level Reviews Using Machine Learning Algorithms

Open Access Peer Reviewed

Authors

  • FRANCIS SHAMLI S1
  • ANBURAJA P2
  • AKASHDURAI S3
  • DHAKSHANAMOORTHY S4
  • MAHENDRAN D5

12345Dhanalakshmi Srinivasan Engineering college, Perambalur / Anna University, Tamilnadu, India.

Article Information

DOI
No DOI
Published Online
March-April 2022
Pages
103-106
Article Type
Research Article
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No DOI available for cross-reference

References

[1] Telemedicine, https://www.mohfw.gov.in/pdf/Telemedicine.pdf
[2] Wittich CM, Burkle CM, Lanier WL. Medication errors: an overview for clinicians. Mayo Clin Proc. 2014 Aug;89(8):1116-25.
[3] CHEN, M. R., & WANG, H. F. (2013). The reason and prevention of hospital medication errors. Practical Journal of Clinical
Medicine, 4.
[4] Drug Review Dataset, https://archive.ics.uci.edu/ml/datasets/Drug% 2BReview%2BDataset%2B%2528Drugs.com%2529#
[5] Fox, Susannah, and Maeve Duggan. ”Health online 2013. 2013.” URL: http://pewinternet.org/Reports/2013/Health-online.aspx
[6] Bartlett JG, Dowell SF, Mandell LA, File TM Jr, Musher DM, Fine MJ. Practice guidelines for the management of communityacquired pneumonia in adults. Infectious Diseases Society of America. Clin Infect Dis. 2000 Aug;31(2):347-82. doi: 10.1086/313954.
Epub 2000 Sep 7. PMID: 10987697; PMCID: PMC7109923.
[7] Fox, Susannah & Duggan, Maeve. (2012). Health Online 2013. Pew Research Internet Project Report.
[8] Felix Gräßer, Stefanie Beckert, Denise Küster, Susanne Abraham, Hagen Malberg, Jochen Schmitt, and Sebastian Zaunseder. 2017.
Neighborhood-based Collaborative Filtering for Therapy Decision Support. In Proceedings of the 2nd International Workshop on
Health Recommender Systems co-located with the 11th International Conference on Recommender Systems Italy, August 31, 2017. 22–
26.
[9] Ioannis Korkontzelos, Azadeh Nikfarjam, Matthew Shardlow, Abeed Sarker, Sophia Ananiadou, and Graciela H. Gonzalez. 2016.
Analysis of the Effect of Sentiment Analysis on Extracting Adverse Drug Reactions from Tweets and Forum Posts. J. of Biomedical
Informatics 62, C (Aug. 2016), 148–158. https: //doi.org/10.1016/j.jbi.2016.06.007
[10] Robert Leaman, Laura Wojtulewicz, Ryan Sullivan, Annie Skariah, Jian Yang, and Graciela Gonzalez. 2010. Towards Internet-age
Pharmacovigilance: Extracting Adverse Drug Reactions from User Posts to Health-related Social Networks. In Proceedings of the 2010
Workshop on Biomedical Natural Language Processing (BioNLP ’10). Association for Computational Linguistics, Stroudsburg, PA,
USA, 117–125. http://dl.acm.org/citation.cfm?id=1869961.1869976
[11] Bing Liu. 2012. Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers.
[12] A. Mishra, A. Malviya, and S. Aggarwal. 2015. Towards Automatic Pharmacovigilance: Analysing Patient Reviews and Sentiment
on Oncological Drugs. In 2015 IEEE International Conference on Data Mining Workshop (ICDMW). 1402–1409.
https://doi.org/10.1109/ICDMW.2015.230
[13] Jin-Cheon Na and Wai Yan Min Kyaing. 2015. Sentiment Analysis of UserGenerated Content on Drug Review. Journal of
Information Science Theory and Practice 1, 1 (Mar 2015). https://doi.org/10.1633/JISTaP.2015.3.1.1
[14] Azadeh Nikfarjam and Graciela H. Gonzalez. 2011. Pattern mining for extraction of mentions of Adverse Drug Reactions from user
comments. AMIA Annual Symposium proceedings 2011 (2011), 1019–1026.
[15] Azadeh Nikfarjam, Abeed Sarker, Karen O’Connor, Rachel E. Ginn, and Graciela Gonzalez. 2015. Pharmacovigilance from social
media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features.
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