Vol. 4 No. 1 (2024): Journal of AI-Assisted Scientific Discovery
Articles

Machine Learning Approaches for Drug Adverse Event Detection: Utilizes machine learning algorithms to detect adverse events associated with drugs from real-world data

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  • Dr. Thomas Müller
Dr. Thomas Müller
Associate Professor of Bioinformatics, University of Vienna, Austria
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Published 08-06-2024

Keywords

  • Machine Learning,
  • Drug Adverse Events,
  • Real-World Data,
  • Healthcare,
  • Natural Language Processing,
  • Supervised Learning,
  • Unsupervised Learning,
  • Data Quality,
  • Bias
    • ...More
    Less
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Thomas Müller, “Machine Learning Approaches for Drug Adverse Event Detection: Utilizes machine learning algorithms to detect adverse events associated with drugs from real-world data”, Journal of AI-Assisted Scientific Discovery, vol. 4, no. 1, pp. 78–85, Jun. 2024, Accessed: Nov. 22, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/22

Abstract

This research paper explores the application of machine learning (ML) algorithms for detecting adverse events related to drugs using real-world data. Adverse drug events (ADEs) are a significant concern in healthcare, often leading to patient morbidity and mortality. Traditional methods of ADE detection rely heavily on manual reporting, which can be limited in scope and accuracy. ML offers a promising approach to enhance ADE detection by analyzing vast amounts of diverse data sources. This paper discusses various ML techniques, including supervised and unsupervised learning, as well as natural language processing (NLP) for text mining in health records. We review the challenges, such as data quality and bias, and propose future directions for improving ADE detection using ML.

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References

  1. Harpaz, Rave, et al. "Mining electronic health records for adverse drug effects with ensemble learning." Journal of biomedical informatics, vol. 58, 2015, pp. 90-99.
  2. Freifeld, Clark C., et al. "Monitoring Twitter for public health safety signals." AMIA Annual Symposium Proceedings, vol. 2014, 2014, pp. 587-596.
  3. Kuhn, Max, et al. "The SIDER database of drugs and side effects." Nucleic acids research, vol. 44, no. D1, 2016, pp. D1075-D1079.
  4. Hripcsak, George, and David J. Albers. "Next-generation phenotyping of electronic health records." Journal of the American Medical Informatics Association, vol. 20, no. 1, 2013, pp. 117-121.
  5. Khare, Ritu, and David Li. "Adverse drug event detection in Twitter data with a self-taught learning approach." Journal of biomedical informatics, vol. 58, 2015, pp. 174-182.
  6. White, Richard W., et al. "Adverse drug events and medication problems in "Hospitalized" patients identified by screening electronic health records: clinical and economic impact." Journal of Patient Safety, vol. 8, no. 3, 2012, pp. 135-140.
  7. Hochberg, Aaron M., et al. "Precision medicine: opportunities, possibilities, and challenges for patients and providers." Journal of the American Medical Informatics Association, vol. 22, no. 4, 2015, pp. 787-795.
  8. Tatonetti, Nicholas P., et al. "Detecting drug interactions from adverse-event reports: interaction between paroxetine and pravastatin increases blood glucose levels." Clinical Pharmacology & Therapeutics, vol. 90, no. 1, 2011, pp. 133-142.
  9. Harpaz, Rave, et al. "Performance of pharmacovigilance signal-detection algorithms for the FDA adverse event reporting system." Clinical Pharmacology & Therapeutics, vol. 93, no. 6, 2013, pp. 539-546.
  10. Maruthi, Srihari, et al. "Deconstructing the Semantics of Human-Centric AI: A Linguistic Analysis." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 11-30.
  11. Dodda, Sarath Babu, et al. "Ethical Deliberations in the Nexus of Artificial Intelligence and Moral Philosophy." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 31-43.
  12. Zanke, Pankaj. "AI-Driven Fraud Detection Systems: A Comparative Study across Banking, Insurance, and Healthcare." Advances in Deep Learning Techniques 3.2 (2023): 1-22.
  13. Biswas, A., and W. Talukdar. “Robustness of Structured Data Extraction from In-Plane Rotated Documents Using Multi-Modal Large Language Models (LLM)”. Journal of Artificial Intelligence Research, vol. 4, no. 1, Mar. 2024, pp. 176-95, https://thesciencebrigade.com/JAIR/article/view/219.
  14. Maruthi, Srihari, et al. "Toward a Hermeneutics of Explainability: Unraveling the Inner Workings of AI Systems." Journal of Artificial Intelligence Research and Applications 2.2 (2022): 27-44.
  15. Biswas, Anjanava, and Wrick Talukdar. "Intelligent Clinical Documentation: Harnessing Generative AI for Patient-Centric Clinical Note Generation." arXiv preprint arXiv:2405.18346 (2024).
  16. Yellu, Ramswaroop Reddy, et al. "AI Ethics-Challenges and Considerations: Examining ethical challenges and considerations in the development and deployment of artificial intelligence systems." African Journal of Artificial Intelligence and Sustainable Development 1.1 (2021): 9-16.
  17. Maruthi, Srihari, et al. "Automated Planning and Scheduling in AI: Studying automated planning and scheduling techniques for efficient decision-making in artificial intelligence." African Journal of Artificial Intelligence and Sustainable Development 2.2 (2022): 14-25.
  18. Ambati, Loknath Sai, et al. "Impact of healthcare information technology (HIT) on chronic disease conditions." MWAIS Proc 2021 (2021).
  19. Singh, Amarjeet, and Alok Aggarwal. "Assessing Microservice Security Implications in AWS Cloud for to implement Secure and Robust Applications." Advances in Deep Learning Techniques 3.1 (2023): 31-51.
  20. Zanke, Pankaj. "Enhancing Claims Processing Efficiency Through Data Analytics in Property & Casualty Insurance." Journal of Science & Technology 2.3 (2021): 69-92.
  21. Pulimamidi, R., and G. P. Buddha. "Applications of Artificial Intelligence Based Technologies in The Healthcare Industry." Tuijin Jishu/Journal of Propulsion Technology 44.3: 4513-4519.
  22. Dodda, Sarath Babu, et al. "Conversational AI-Chatbot Architectures and Evaluation: Analyzing architectures and evaluation methods for conversational AI systems, including chatbots, virtual assistants, and dialogue systems." Australian Journal of Machine Learning Research & Applications 1.1 (2021): 13-20.
  23. Modhugu, Venugopal Reddy, and Sivakumar Ponnusamy. "Comparative Analysis of Machine Learning Algorithms for Liver Disease Prediction: SVM, Logistic Regression, and Decision Tree." Asian Journal of Research in Computer Science 17.6 (2024): 188-201.
  24. Maruthi, Srihari, et al. "Language Model Interpretability-Explainable AI Methods: Exploring explainable AI methods for interpreting and explaining the decisions made by language models to enhance transparency and trustworthiness." Australian Journal of Machine Learning Research & Applications 2.2 (2022): 1-9.
  25. Dodda, Sarath Babu, et al. "Federated Learning for Privacy-Preserving Collaborative AI: Exploring federated learning techniques for training AI models collaboratively while preserving data privacy." Australian Journal of Machine Learning Research & Applications 2.1 (2022): 13-23.
  26. Zanke, Pankaj. "Machine Learning Approaches for Credit Risk Assessment in Banking and Insurance." Internet of Things and Edge Computing Journal 3.1 (2023): 29-47.
  27. Maruthi, Srihari, et al. "Temporal Reasoning in AI Systems: Studying temporal reasoning techniques and their applications in AI systems for modeling dynamic environments." Journal of AI-Assisted Scientific Discovery 2.2 (2022): 22-28.
  28. Yellu, Ramswaroop Reddy, et al. "Transferable Adversarial Examples in AI: Examining transferable adversarial examples and their implications for the robustness of AI systems." Hong Kong Journal of AI and Medicine 2.2 (2022): 12-20.
  29. Reddy Yellu, R., et al. "Transferable Adversarial Examples in AI: Examining transferable adversarial examples and their implications for the robustness of AI systems. Hong Kong Journal of AI and Medicine, 2 (2), 12-20." (2022).
  30. Zanke, Pankaj, and Dipti Sontakke. "Artificial Intelligence Applications in Predictive Underwriting for Commercial Lines Insurance." Advances in Deep Learning Techniques 1.1 (2021): 23-38.
  31. Singh, Amarjeet, and Alok Aggarwal. "Artificial Intelligence Enabled Microservice Container Orchestration to increase efficiency and scalability for High Volume Transaction System in Cloud Environment." Journal of Artificial Intelligence Research and Applications 3.2 (2023): 24-52.

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