Predicting medical drug usage intentions via SGD-based text classification model

Abstract

The effects of medical drugs and their usage purposes vary among individuals due to the chemical composition of drugs, side effects, genetics, etc. Even if those effects are to be discovered pharmacologically, they cannot be fully understood. Hence, it becomes essential to analyze the individuals’ reviews and experiences to unearth such effects and find out which other purposes drugs are used for, in addition to the target disease they are developed to cure. Text classification methods present various solutions to analyze those reviews effectively. Generally, these effects are investigated in terms of emotional analysis of medical drug usage experience as positive or negative. However, some drugs can be used for more than one specific treatment. For example, an antipsychotic drug can be used for both depression and anxiety or ADHD. Therefore, the effects of medical drug users and drug names to be associated with the review of the studies should be covered comprehensively. Based on this motivation, this study proposed a lightweight model for the prediction of medical drug usage intentions using text-based patient reviews. For this purpose, TF-IDF and bigram methods are used for text classification in the feature extraction step, then the Stochastic Gradient Descent (SGD) classifier is used for prediction and compared to other popular machine learning algorithms. Classification results indicate that the SGD and TF-IDF-Bigram approach effectively predicts drug usage intentions for medical purposes with an accuracy of 98.42%. Based on the outcomes, it is concluded that the findings of this study may be beneficial in pharmaceutics or medicine considering drug design, reducing side effects, health management, treatment adherence and process design, and personalized medicine.

Keywords

• Drug usage intention
• Machine Learning
• Medical Drug Usage
• Text Classification
• Text Mining

References

1. 1. Şen Ö., S.Bozkurt Keser, and K. Keskin, Early stage diabetes prediction using decision tree-based Ensemble Learning Model. International Advanced Researches and Engineering Journal, 2023. 7(1): p. 62-71.
2. 2. Jahan S., Major depressive disorder diagnosis from electroencephalogram data and potential treatment with dimethyltryptamine. International Advanced Researches and Engineering Journal, 2023. 7(2):p. 90–96.
3. 3. Tuncer T., E. Aydemir, F. Özyurt, S. Dogan, S. B. Belhaouarı, and E. Akbal, An automated COVID-19 respiratory sound classification method based on novel local symmetric Euclidean distance pattern and Relieff Iterative MRMR feature selector. International Advanced Researches and Engineering Journal, 2021. 5(3): p.334–343.
4. 4. Liu R.-L., Text classification for healthcare information support, in 20th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems. 2007. Kyoto, Japan: p. 44–53.
5. 5. Billyan B., R. Sarno, K.R. Sungkono, and I.R. Tangkawarow, Fuzzy k-nearest neighbor for restaurants business sentiment analysis on TripAdvisor, in 2019 International Conference on Information and Communications Technology. 2019. Kuala Lumpur, Malaysia: p. 543-548.
6. 6. Pratama B. Y. and R. Sarno, Personality classification based on Twitter text using naive Bayes, KNN and SVM, in 2015 International Conference on Data and Software Engineering (ICoDSE), 2015. Yogyakarta, Indonesia: p. 170-174.
7. 7. Suela O-M., M. Zampieri, S. Malmasi, M. Vela, L.P. Dinu, and J. van Genabith [cited 2024 1 Jun]; Available from: https://arxiv.org/abs/1710.09306
8. 8. Olsson J. S., D. W. Oard, and J. Hajič, Cross-language text classification, in Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, 2005. Salvador Brazil: p.645-646.

9. 9. Li C.H. and S.C. Park, An efficient document classification model using an improved back propagation neural network and singular value decomposition. Expert Systems with Applications, 2009. 36(2): p.3208–3215.
10. 10. Tang B., H. He, P. M. Baggenstoss, and S. Kay, A bayesian classification approach using class-specific features for text categorization. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(6):p. 1602–1606.
11. 11. Kumar R. R., M. B. Reddy, and P. Praveen, Text Classification Performance Analysis on Machine Learning. International Journal of Advanced Science and Technology, 2019. 28(20): p. 691–697.
12. 12. Shen P., H. Wang, Z. Meng, Z. Yang, Z. Zhi, R. Jin, and A. Yang, An Improved Parallel Bayesian Text Classification Algorithm. Review of Computer Engineering Studies, 2016. 3(1): p. 6–10.
13. 13. Dalal M. K. and M.A. Zaveri, Automatic text classification: A technical review. International Journal of Computer Applications, 2011. 28(2): p. 37–40.
14. 14. Shokrpour N., R. Rezaee, R. Akbari, M. Nasiri, and F. Foroughinia, An evaluation of classification algorithms for prediction of drug interactions: Identification of the best algorithm. International Journal of Pharmaceutical Investigation, 2018. 8(2): p. 92-99.
15. 15. Chai Z., X. Wan, Z. Zhang, and M. Li, Harvesting drug effectiveness from social media, in Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019. Paris, France: p.55-64.
16. 16. Aramaki E., Y. Miura, M. Tonoike, T. Ohkuma, H. Masuichi, K. Waki, and K. Ohe, Extraction of adverse drug effects from clinical records. Studies in health technology and informatics, 2010. 160(1): p.739–743.
17. 17. Gräßer F., S. Kallumadi, H. Malberg, and S. Zaunseder, Aspect-based sentiment analysis of drug reviews applying cross-domain and cross-data learning, in Proceedings of the 2018 International Conference on Digital Health, 2018. Lyon France: p.121-125.
18. 18. Lavecchia A., Machine-learning approaches in drug discovery: Methods and applications. Drug Discovery Today, 2015. 20(3): p.318–331.
19. 19. Abada W., A. Bouramoul, and M. S. Kahil, How machine learning algorithms can examine patterns in multiple substance addictions: Predicting the effects and interactions of psychoactive substances. International Journal of Computers and Applications, 2024. 46(11): p.1045–1055.
20. 20. Elahi E., E. Elahi, S. Anwar, B. Shah, Z. Halim, A. Ullah, I. Rida, M. Waqas, Knowledge graph enhanced contextualized attention-based network for responsible user-specific recommendation. ACM Transactions on Intelligent Systems and Technology, 2024. 15(4): p. 1–24.
21. 21. Korkmaz T., A. Çetinkaya, H. Aydin, and M. A. Barişkan, Analysis of whether news on the internet is real or fake by using deep learning methods and the TF-IDF algorithm. International Advanced Researches and Engineering Journal, 2021. 5(1): p. 31–41.
22. 22. Zhang T., Solving large scale linear prediction problems using stochastic gradient descent algorithms, in Twenty-first international conference on Machine learning- ICML ’04, 2004. Banff, Alberta, Canada: p.116-124.
23. 23. Al-Hadhrami S., T. Vinko , T. Al-Hadhrami, F. Saeed, and S.N. Qasem, Deep learning-based method for sentiment analysis for patients’ drug reviews. PeerJ Computer Science, 2024. 10: p. e1976.
24. 24. Dandala B., V. Joopudi, and M. Devarakonda, Adverse Drug Events Detection in clinical notes by jointly modeling entities and relations using Neural Networks. Drug Safety, 2019. 42(1): p.135–146.
25. 25. Colón-Ruiz C. and I. Segura-Bedmar, Comparing deep learning architectures for sentiment analysis on drug reviews. Journal of Biomedical Informatics, 2020. 110: p.103539.