Mining adverse events in large frequency tables with ontology, with an application to the vaccine adverse event reporting system

Abstract

Many statistical methods have been applied to VAERS (vaccine adverse event reporting system) database to study the safety of COVID‐19 vaccines. However, none of these methods considered the adverse event (AE) ontology. The AE ontology contains important information about biological similarities between AEs. In this paper, we develop a model to estimate vaccine‐AE associations while incorporating the AE ontology. We model a group of AEs using the zero‐inflated negative binomial model and then estimate the vaccine‐AE association using the empirical Bayes approach. This model handles the AE count data with excess zeros and allows borrowing information from related AEs. The proposed approach was evaluated by simulation studies and was further illustrated by an application to the Vaccine Adverse Event Reporting System (VAERS) dataset. The proposed method is implemented in an R package available at https://github.com/umich-biostatistics/zGPS.AO.

COVID-19 vaccine adverse events: Evaluating the pathophysiology from an undersulfated and degraded glycocalyx perspective

“…Although most recipients of the COVID-19 vaccines seem to have experienced no adverse effects, more and more reports of severe AEs, and death, due to COVID-19 vaccination are being confirmed. Factors contributing to the variation in AEs experienced by COVID-19 vaccine recipients include underreporting, limitations of adverse event reporting systems 122,331 , and the difficulty in determining the true effects of vaccines. To further complicate matters, Haas et al reported that although significantly more AEs were reported in the vaccine recipient groups compared to the placebo groups, the rates of reported AEs in the placebo arms were still considerable 332 .…”

Supervision System of AI-based Software as a Medical Device

“…Compared with relatively mature pre-market access, we believe that as medical software developers do not fully understand the clinical environment in terms of errors and risks, we should not overlook scientifically and actively guiding and supervising the marketed development of AI-based SaMDs through regulation, preventing algorithmic risks of AI-based SaMDs, and actively addressing ongoing regulation in post-access AI-based SaMD applications as the number of products applied for and marketed has been increasing. From October 2018 to May 2019, medical device recalls caused by software defects accounted for 16.91% of the 136 international medical device recalls resulting from serious adverse events in the US, UK, Canada, Australia, and China [12]. Unfortunately, the number of such events will undoubtedly increase with the black-box usage model of AI-based SaMDs.…”