Machine Learning Approaches for Analyzing FAERS Data: Advancing Fetal Health Monitoring and Drug Safety
Emily Grace Mitchell
Charles Sturt University
Fazle Rabbi
Australian Computer Society, Australia
Keywords: FDA, Adverse Event Reporting System, FAERS, Machine Learning, Fetal Health Monitoring, Drug Safety
Abstract
The FDA Adverse Event Reporting System (FAERS) is a valuable resource for monitoring drug safety and identifying potential adverse events. This study explores the application of machine learning approaches to analyze FAERS data and their implications for fetal health monitoring and drug safety. Several machine learning techniques are proposed for this purpose.Text mining and natural language processing (NLP) techniques are utilized to extract relevant information from narrative descriptions in FAERS data. This includes drug names, adverse events, and patient demographics, enabling the identification of drugs associated with specific adverse events in pregnant women.Signal detection is enhanced through the integration of traditional methods like disproportionality analysis with machine learning algorithms. By analyzing FAERS data, potential associations between drugs and adverse events specific to fetal health can be accurately identified, improving drug safety surveillance.The integration of FAERS data with other healthcare datasets, such as electronic health records (EHRs) or birth registries, is explored using machine learning approaches. By harmonizing these diverse datasets, patterns and relationships between drug exposures during pregnancy and adverse fetal outcomes can be identified.Predictive modeling is developed by leveraging FAERS data and other relevant variables to forecast adverse fetal outcomes based on drug exposure data. This aids in early detection of potential risks associated with specific drugs and identification of factors contributing to fetal harm.Temporal analysis of FAERS data, employing machine learning techniques, uncovers patterns and trends in adverse events related to fetal health over time. This enables the identification of emerging risks, monitoring the impact of regulatory actions, and assessing the effectiveness of interventions aimed at improving drug safety during pregnancy.Causal inference methods, such as propensity score matching and instrumental variable analysis, are applied to FAERS data to estimate causal relationships between drug exposures and adverse fetal outcomes. These approaches address confounding factors and provide robust evidence of drug safety or risk.Machine learning analysis of FAERS data requires domain expertise, careful validation, and consideration of data limitations.These techniques have the potential to enhance fetal health monitoring and improve drug safety surveillance by leveraging the rich information available in the FAERS database.
