Leveraging Machine Learning Techniques for Predicting Adverse Drug Reactions: A Comprehensive Review

Abstract

Background: Adverse drug reactions (ADRs) pose significant risks to patient safety and healthcare outcomes. As the complexity of medications and patient conditions increases, traditional methods of predicting ADRs are often insufficient. The integration of machine learning (ML) techniques into healthcare offers a promising approach to enhance the prediction and management of ADRs.

Methods: This review systematically examines peer-reviewed literature published from 2009 to 2023, focusing on ML applications in predicting ADRs. Databases such as PubMed and Web of Science were utilized to gather relevant studies. Key criteria for inclusion involved the use of ML models to analyze clinical data, including electronic health records and patient-reported outcomes.

Results: The analysis identified 53 studies that employed a variety of ML algorithms, including support vector machines (SVM), artificial neural networks (ANN), and natural language processing (NLP) techniques. The majority of these studies reported significant improvements in the accuracy of ADR predictions compared to traditional methods. Notably, SVM and NLP were the most frequently utilized models, demonstrating strong effectiveness in extracting relevant insights from complex datasets.

Conclusion: The findings underscore the potential of machine learning to revolutionize the prediction of adverse drug reactions, ultimately enhancing patient safety. However, challenges remain regarding data standardization, algorithm interpretability, and the integration of these models into clinical practice. Future research should focus on developing standardized metrics for evaluating ML performance in ADR prediction, as well as exploring the real-world applicability of these technologies in diverse healthcare settings.