Pharmacovigilance in the digital era : A machine learning approach for early detection of adverse drug reactions
*B. K. PandaCorresponding authorpandabijoy@hotmail.comDepartment of Pharmacy PracticeKrishna Vishwa Vidyapeeth (Deemed to be University)Krishna Institute of PharmacyKarad, Maharashtra, IndiaView full profile → , Pornima B. Niranjanepornimaniranjane@gmail.comDepartment of Computer Science & EnggineringBabasaheb Naik College of EngineeringPusad, Maharashtra, IndiaView full profile → , D. P. Malidipakpmali19@gmail.comDepartment of Pharmaceutical ChemistryKrishna Vishwa Vidyapeeth (Deemed to be University)Krishna Institute of PharmacyKarad, Maharashtra, IndiaView full profile → , Prachi A. Bainalwarprachidussawar1987@gmail.comDepartment of Computer TechnologyYeshwantrao Chavan College of EngineeringNagpur, Maharashtra, IndiaView full profile → , Ujjwala Bal Aherujjwalaaher@gmail.comDepartment of Computer EngineeringGovernment PolytechnicNagpur, Maharashtra, IndiaView full profile → , Saurabh Bhattacharyababu.saurabh@gmail.comDepartment of Computer ApplicationsNational Institute of TechnologyRaipur, Chhattisgarh, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Published Online:
- 30 Mar 2024
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JSMS-1267
- Pages:
- 429–440
Abstract
Keywords
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References
[1] X. Wang, X. Wang and S. Zhang, “Adverse Drug Reaction Detection From Social Media Based on Quantum Bi-LSTM With Attention,” in IEEE Access, vol. 11, pp. 16194-16202 (2023).
[2] A. K. Tripathy, N. Joshi, H. Kale, M. Durando and L. Carvalho, “Detection of adverse drug events through data mining techniques,” 2015 International Conference on Technologies for Sustainable Development (ICTSD), Mumbai, India (2015).
[3] Y. Ji et al., “A Potential Causal Association Mining Algorithm for Screening Adverse Drug Reactions in Postmarketing Surveillance,” in IEEE Transactions on Information Technology in Biomedicine, vol. 15, no. 3, pp. 428-437, May (2011).
[4] Y. -P. Xiang et al., “Rapid Assessment of Adverse Drug Reactions by Statistical Solution of Gene Association Network,” in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 12, no. 4, pp. 844-850, 1 July-Aug. (2015).
[5] D. Abin, T. C. Mahajan, M. S. Bhoj, S. Bagde and K. Rajeswari, “Causal Association Mining for Detection of Adverse Drug Reactions,” International Conference on Computing Communication Control and Automation, Pune, India, 2015, pp. 382-385 (2015).
[6] W. -Y. Lin and C. -F. Lo, “Co-training and ensemble based duplicate detection in adverse drug event reporting systems,” 2013 IEEE International Conference on Bioinformatics and Biomedicine, Shanghai, China, pp. 7-8 (2013).
[7] J. Zhu, Y. Liu, Y. Zhang and D. Li, “Attribute Supervised Probabilistic Dependent Matrix Tri-Factorization Model for the Prediction of Adverse Drug-Drug Interaction,” in IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 7, pp. 2820-2832, July (2021).
[8] A. A. Pandit and S. A. Dubey, “A comprehensive review on Adverse Drug Reactions (ADRs) Detection and Prediction Models,” 2021 13th International Conference on Computational Intelligence and Communication Networks (CICN), Lima, Peru, pp. 123-127 (2021).
[9] Dataset: https://www.kaggle.com/code/tunahandeniz/medicine-usage-side-effects-analysis/input
[10] S. U. Khan, I. U. Haq, S. Rho, S. W. Baik and M. Y. Lee, “Cover the violence: A novel deep-learning-based approach towards violence-detection in movies”, Appl. Sci., vol. 9, no. 22, pp. 4963, Nov. (2019).
