Fusing Deep Learning Algorithms to Predict COVID-19 Vaccination Adverse Events of Recovered, Hospitalized or Died

抄録

While COVID-19 poses significant global challenges, this study proposes leveraging deep learning models to predict adverse events post-vaccination. Using the Vaccine Adverse Event Reporting System (VAERS) dataset, which includes potential side effects of Pfizer, Janssen, and Moderna vaccines, the study partitions the data to predict vaccination adverse events (VAE) such as "died," "hospitalized," and "recovered." The proposed DeepCNNBDLSTM model combines deep neural network layers with convolutional and bidirectional long short-term memory layers. Baseline models include deep neural networks, bidirectional long short-term memory, convolutional neural networks, and long short-term memory. Performance metrics such as confusion matrix, AUCROC, accuracy, F1-score, precision, and recall are evaluated. Experimental results show the proposed model outperforms by achieving an accuracy of 89.05% for predicting 'recovered' VAE (trained on Pfizer dataset), 99.03% accuracy for predicting 'hospitalized' VAE, and 99.86% accuracy for predicting 'died' VAE (both trained on Moderna dataset). These insights may aid doctors in selecting the most effective COVID-19 vaccine for patient protection.