PRE-PROCESSING TECHNIQUES FOR INTEGRATED ADVERSE DRUG REACTION DATASETS

Abstract

Objective: To integrate and preprocess datasets from the FDA adverse event reporting system (FAERS), side effect resource (SIDER), DrugBank, and PubChem to extract meaningful insights into drug interactions, adverse events, and molecular properties, thereby supporting drug discovery and pharmacovigilance. Methods: The study implements a preprocessing pipeline that includes data cleaning, normalization, and harmonization to ensure consistency across the diverse datasets. Standardization of drug nomenclature and handling of missing or inconsistent information are emphasized. The integrated data is then subjected to exploratory data analysis and advanced visualization techniques to uncover patterns and correlations within the data. Results: The integration and preprocessing of the datasets improved the consistency and quality of the drug-related data. Exploratory analysis revealed patterns and potential associations among drugs, adverse events, and molecular features. Visualization tools effectively conveyed complex relationships and significant trends, enhancing interpretability. Conclusion: The study successfully demonstrates that integrating and preprocessing multiple drug-related datasets improves data quality and facilitates comprehensive analysis. The resulting resource supports better-informed decision-making in drug development and pharmacovigilance by enabling a deeper understanding of drug interactions and safety profiles