Autoencoder outlier detection during the Covid-19 vaccination campaign
Abstract: Outlier detection in high-dimensional data is a complex task, useful in many fields. One major application is in healthcare, where the high-dimensional healthcare registers can be used to detect patterns related to the medical behaviour and state of the population. In this project, neural network based autoencoders were used to study the Covid-19 vaccination campaign, where it was trained on healthcare data from unvaccinated individuals. Outliers were selected by thresholding the reconstruction error from the autoencoder, both for vaccinated and unvaccinated individuals, to compare the data points. The outliers were in principle selected based on the dimensionality of their input vector, and the frequency of representation in the training data set. The previously known myocarditis cases in the young male population, as a side effect of the mRNA Covid-19 vaccines, was used to model the method. The method detected various patterns, mainly vaccination patterns of the population and the vaccine effect. Outlier detection in high-dimensional healthcare registers can possibly detect patterns before statistical models, and search the data for non-linear correlations.
AT THIS PAGE YOU CAN DOWNLOAD THE WHOLE ESSAY. (follow the link to the next page)