Using machine learning for personalized prediction of longitudinal coronavirus disease 2019 vaccine responses in transplant recipients

The coronavirus disease 2019 pandemic has underscored the importance of vaccines, especially for immunocompromised populations like solid organ transplant recipients, who often have weaker immune responses. The purpose of this study was to compare deep learning architectures for predicting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine responses 12 months postvaccination in this high-risk group. Using data from 303 solid organ transplant recipients from a Canadian multicenter cohort, models were developed to forecast anti–receptor-binding domain antibody levels. The study compared traditional machine learning models—logistic regression, epsilon-support vector regression, random forest regressor, and gradient boosting regressor—and deep learning architectures, including long short-term memory (LSTM), recurrent neural networks, and a novel model, routed LSTM. This new model combines capsule networks with LSTM to reduce the need for large data sets. Demographic, clinical, and transplant-specific data, along with longitudinal antibody measurements, were incorporated into the models. The routed LSTM performed best, achieving a mean square error of 0.02 ± 0.02 and a Pearson correlation coefficient of 0.79 ± 0.24, outperforming all other models. Key factors influencing vaccine response included age, immunosuppression, breakthrough infection, body mass index, sex, and transplant type. These findings suggest that artificial intelligence could be a valuable tool in tailoring vaccine strategies, improving health outcomes for vulnerable transplant recipients.