An Ensemble Model for Detection of Adverse Drug Reactions

Nafea, Ahmed A. and Ibrahim, Mustafa S. and Mukhlif, Abdulrahman A. and AL-Ani, Mohammed M. and Omar, Nazlia (2024) An Ensemble Model for Detection of Adverse Drug Reactions. ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 12 (1). pp. 41-47. ISSN 2410-9355

Text (Research Article) ARO.11403.VOL12.NO1.2024.ISSUE22-PP41-47.pdf - Published Version Available under License Creative Commons Attribution Non-commercial Share Alike. Download (532kB)

Abstract

The detection of adverse drug reactions (ADRs) plays a necessary role in comprehending the safety and benefit profiles of medicines. Although spontaneous reporting stays the standard approach for ADR documents, it suffers from significant under reporting rates and limitations in terms of treatment inspection. This study proposes an ensemble model that combines decision trees, support vector machines, random forests, and adaptive boosting (ADA-boost) to improve ADR detection. The experimental evaluation applied the benchmark data set and many preprocessing techniques such as tokenization, stop-word removal, stemming, and utilization of Point-wise Mutual Information. In addition, two term representations, namely, term frequency-inverse document frequency and term frequency, are utilized. The proposed ensemble model achieves an F-measure of 89% on the dataset. The proposed ensemble model shows its ability in detecting ADR to be a favored option in achieving both accuracy and clarity.

Item Type:	Article
Additional Information:	Ahanin, Z., and Ismail, M.A., 2022. A multi-label emoji classification method using balanced pointwise mutual information-based feature selection. Computer Speech and Language, 73, p.101330. DOI: https://doi.org/10.1016/j.csl.2021.101330 Alheeti, K.M.A., Alzahrani, A., Alamri, M., Kareem, A.K., and Al_Dosary, D., 2023. A comparative study for SDN security based on machine learning. International Journal of Interactive Mobile Technologies, 17(11), pp.131-140. DOI: https://doi.org/10.3991/ijim.v17i11.39065 Alsumaidaie, M.S.I., Alheeti, K.M.A., and Al-Aloosy, A.K., 2023. Intelligent Detection System for a Distributed Denial-of-Service (DDoS) Attack Based on Time Series. In: 2023 15th International Conference on Developments in eSystems Engineering (DeSE). IEEE, pp.445-450. DOI: https://doi.org/10.1109/DeSE58274.2023.10100180 Alsumaidaie, M.S.I., Alheeti, K.M.A., and Alaloosy, A.K., 2023. Intelligent detection of distributed denial of service attacks: A supervised machine learning and ensemble approach. Iraqi Journal for Computer Science and Mathematics, 4(3), pp.12-24. Azam, N., and Yao, J., 2012. Comparison of term frequency and document frequency based feature selection metrics in text categorization. Expert Systems with Applications, 39(5), pp.4760-4768. DOI: https://doi.org/10.1016/j.eswa.2011.09.160 Bassel, A., Abdulkareem, A.B., Alyasseri, Z.A.A., Sani, N.S., and Mohammed, H.J., 2022. Automatic malignant and benign skin cancer classification using a hybrid deep learning approach. Diagnostics (Basel), 12(10), p.2472. Brueckle, M.S., Thomas, E.T., Seide, S.E., Pilz, M., Gonzalez-Gonzalez, A.I., Dinh, T.S., Gerlach, F.M., Harder, S., Glasziou, P.P., and Muth, C., 2023. Amitriptyline’s anticholinergic adverse drug reactions-a systematic multipleindication review and meta-analysis. PLoS One, 18(4), p.e0284168. DOI: https://doi.org/10.1371/journal.pone.0284168 Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., and Lopez A., 2020. A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408, pp.189-215. DOI: https://doi.org/10.1016/j.neucom.2019.10.118 Charbuty, B., and Abdulazeez, A., 2021. Classification based on decision tree algorithm for machine learning. Journal of Applied Science and Technology Trends, 2(01), pp.20-28. DOI: https://doi.org/10.38094/jastt20165 Ebrahimi, M., Yazdavar, A.H., Salim, N., and Eltyeb, S., 2016. Recognition of side effects as implicit-opinion words in drug reviews. Online Information Review, 40(7), pp.1018-1032. DOI: https://doi.org/10.1108/OIR-06-2015-0208 Edwards, I.R., and Aronson, J.K., 2000. Adverse drug reactions: Definitions, diagnosis, and management. The Lancet, 356(9237), pp.1255-1259. DOI: https://doi.org/10.1016/S0140-6736(00)02799-9 Kareem, A.K., and Alheeti, K.M.A., 2022. Multimodal Approach for Fall Detection based on Support Vector Machine. In: AIP Conference Proceedings. AIP Publishing. DOI: https://doi.org/10.1063/5.0115534 Kiritchenko, S., Mohammad, S.M., Morin, J., De Bruijn, B., 2017. NRC-Canada at SMM4H Shared Task: Classifying Tweets Mentioning Adverse Drug Reactions and Medication Intake. In: CEUR Workshop Proceedings, p.1-11. Kiritchenko, S., Zhu, X., and Mohammad, S.M., 2014. Sentiment analysis of short informal texts. Journal of Artificial Intelligence Research, 50, pp.723-762. DOI: https://doi.org/10.1613/jair.4272 Li, Z., Yang, Z., Luo, L., Xiang, Y., and Lin, H., 2020. Exploiting adversarial transfer learning for adverse drug reaction detection from texts. Journal of Biomedical Informatics, 106, p.103431. DOI: https://doi.org/10.1016/j.jbi.2020.103431 Martin, G.L., Jouganous, J., Savidan, R., Bellec, A., Goehrs, C., Benkebil, M., Miremont, G., Micallef, J., Salvo, F., Pariente, A., and Létinier, L., 2022. Validation of artificial intelligence to support the automatic coding of patient adverse drug reaction reports, using nationwide pharmacovigilance data. Drug Safety, 45(5), pp.535-548. DOI: https://doi.org/10.1007/s40264-022-01153-8 McMaster, C., Chan, J., Liew, D.F.L., Su, E., Frauman, A.G., Chapman, W.W., and Pires, D.E.V., 2023. Developing a deep learning natural language processing algorithm for automated reporting of adverse drug reactions. Journal of Biomedical Informatics, 137, p.104265. DOI: https://doi.org/10.1016/j.jbi.2022.104265 Mukhlif, A.A., Al-Khateeb, B., and Mohammed, M., 2023. Classification of breast cancer images using new transfer learning techniques. Iraqi Journal for Computer Science and Mathematics, 4(1), pp.167-180. DOI: https://doi.org/10.52866/ijcsm.2023.01.01.0014 Nafea, A.A., Omar, N., and AL-Ani, M.M., 2021. Adverse drug reaction detection using latent semantic analysis. Journal of Computer Science, 17(10), pp.960-970. DOI: https://doi.org/10.3844/jcssp.2021.960.970 Nafea, A.A., Omar, N. and Al-Qfail, Z.M., 2024. Artificial neural network and latent semantic analysis for adverse drug reaction detection. Baghdad Science Journal, 21(1), pp.0226-0233. DOI: https://doi.org/10.21123/bsj.2023.7988 Oyebode, O., and Orji, R., 2023. Identifying adverse drug reactions from patient reviews on social media using natural language processing. Health Informatics Journal, 29(1). DOI: https://doi.org/10.1177/14604582221136712 Pham, B.T., Nguyen, M.D., Nguyen-Thoi, T., Ho, L.S., Koopialipoor, M., Quoc, N.K., Armaghani, D.J., and Van Le H., 2021. A novel approach for classification of soils based on laboratory tests using adaboost, tree and ANN modeling. Transportation Geotechnics, 27, p.100508. DOI: https://doi.org/10.1016/j.trgeo.2020.100508 Shen, C., Li, Z., Chu, Y., and Zhao Z., 2021. GAR: Graph adversarial representation for adverse drug event detection on Twitter. Applied Soft DOI: https://doi.org/10.1016/j.asoc.2021.107324 Computing, 106, p.107324. Sheykhmousa, M., Mahdianpari, M., Mohammadimanesh, F., Ghamisi, P., and Hom, S., 2020. Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, pp.6308-6325. DOI: https://doi.org/10.1109/JSTARS.2020.3026724 Sørup, F.K.H., Eriksson, R., Westergaard, D., Hallas, J., Brunak, S., and Ejdrup Andersen, S., 2020. Sex differences in text-mined possible adverse drug events associated with drugs for psychosis. Journal of Psychopharmacology, 34(5), pp.532-539. DOI: https://doi.org/10.1177/0269881120903466 Wang, C.S., Lin, P.J., Cheng, C.L., Tai, S.H., Kao Yang, Y.H., and Chiang, J.H., 2019. Detecting potential adverse drug reactions using a deep neural network model. Journal of Medical Internet Research, 21(2), p.e11016. DOI: https://doi.org/10.2196/11016 Yadesa, T.M., Kitutu, F.E., Deyno, S., Ogwang, P.E., Tamukong, R., and Alele, P.E., 2021. Prevalence, characteristics and predicting risk factors of adverse drug reactions among hospitalized older adults: A systematic review and meta-analysis. SAGE Open Medicine, 9. DOI: https://doi.org/10.1177/20503121211039099 Yates, A., and Goharian, N., 2013. ADRTrace: Detecting expected and unexpected adverse drug reactions from user reviews on social media sites. In: European Conference on Information Retrieval. Springer, Berlin, Heidelberg, pp.816-819. DOI: https://doi.org/10.1007/978-3-642-36973-5_92 Yousef, R.N., Tiun, S., and Omar, N., 2019. Extended trigger terms for extracting adverse drug reactions in social media texts. Journal of Computer Science, 15(6), pp.873-879. DOI: https://doi.org/10.3844/jcssp.2019.873.879 Yousef, R.N.M., Tiun S., Omar N., and Alshari, E.A., 2020. Lexicon replacement method using word embedding technique for extracting adverse drug reaction. International Journal of Technology Management and Information System, 2(1), pp.113-122. Zhang, T., Lin, H., Ren.,Y., Yang, Z., Wang, J., Duan, X., and Xu, B., 2021. Identifying adverse drug reaction entities from social media with adversarial transfer learning model. Neurocomputing, 453, pp.254-262. DOI: https://doi.org/10.1016/j.neucom.2021.05.007 Zhang, T., Lin, H., Xu, B., Ren, Y., Yang, Z., Wang, J., and Duan, X., 2020. Gated iterative capsule network for adverse drug reaction detection from social media. In: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, pp.387-390. DOI: https://doi.org/10.1109/BIBM49941.2020.9313092
Uncontrolled Keywords:	Adverse drug reactions, Classification, Ensemble Model, Machine Learning, Pointwise Mutual Information
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	ARO-The Scientific Journal of Koya University > VOL 12, NO 1 (2024)
Depositing User:	Dr Salah Ismaeel Yahya
Date Deposited:	02 Sep 2024 06:57
Last Modified:	02 Sep 2024 06:57
URI:	http://eprints.koyauniversity.org/id/eprint/468

Actions (login required)

View Item