Author ORCID Identifier

Defense Date


Document Type


Degree Name

Master of Science


Pharmaceutical Sciences

First Advisor

Dayanjan (Shanaka) Wijesinghe


This thesis aims to tackle the challenges of utilizing the FDA Adverse Event Reporting System (FAERS) for extracting and comprehending drug safety data. FAERS is a repository of vast amounts of complex and unstructured data, which can be challenging to analyze without significant coding experience. To address this issue, this thesis proposes using a low-code platform, such as KNIME, to simplify the analysis of FAERS data. By employing KNIME, even users without coding knowledge can extract and visualize FAERS data, enabling more precise and efficient drug safety data analysis. Following the development of the workflow for extracting data from FAERS, this thesis also investigated a hypothesis-generating approach to evaluate the potential association between Aspirin and bevacizumab regarding adverse events, specifically focusing on the theory that Aspirin could lower the incidence of bevacizumab-induced Hypertension. The aim was to compare the differences between individuals who experienced adverse events while taking Aspirin versus those who did not. The results demonstrate that the workflow successfully identified fewer adverse events among individuals taking Aspirin than those who did not. These findings demonstrate the potential for using FAERS data and the developed workflow to identify the safety signals of any drug and answering clinical questions without the need for coding expertise. While the workflow has been tested and found to be quite effective at extracting data from FAERS, the hypotheses generation capability will require further validation.


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Available for download on Saturday, April 29, 2028