The COVID-19 pandemic has underscored the importance of vaccines, especially for immunocompromised populations like solid organ transplant (SOT) recipients, who often have weaker immune responses. The purpose of this study was to compare deep learning architectures for predicting SARS-CoV-2 vaccine responses 12 months post-vaccination in this high-risk group. Utilizing data from 303 SOT recipients from a Canadian multicenter cohort, models were developed to forecast anti-receptor binding domain (RBD) 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 datasets. 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 (MSE) of 0.02±0.02 and a Pearson correlation coefficient (PCC) of 0.79±0.24, outperforming all other models. Key factors influencing vaccine response included age, immunosuppression, breakthrough infection, BMI, sex, and transplant type. These findings suggest that AI could be a valuable tool in tailoring vaccine strategies, improving health outcomes for vulnerable transplant recipients.

中文翻译：

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使用机器学习对移植受者的纵向 COVID-19 疫苗反应进行个性化预测。


COVID-19 大流行凸显了疫苗的重要性，尤其是对于免疫功能低下人群，如实体器官移植 （SOT） 接受者，他们的免疫反应通常较弱。本研究的目的是比较深度学习架构在预测该高危人群接种疫苗 12 个月后 SARS-CoV-2 疫苗反应的效果。利用来自加拿大多中心队列的 303 名 SOT 受者的数据，开发了模型来预测抗受体结合域 （RBD） 抗体水平。该研究比较了传统的机器学习模型（逻辑回归、epsilon 支持向量回归、随机森林回归和梯度提升回归）和深度学习架构，包括长短期记忆 （LSTM）、递归神经网络和一种新型模型路由 LSTM。这种新模型将胶囊网络与 LSTM 相结合，以减少对大型数据集的需求。人口统计学、临床和移植特异性数据以及纵向抗体测量被纳入模型。路由的 LSTM 表现最佳，均方误差 （MSE） 为 0.02±0.02，皮尔逊相关系数 （PCC） 为 0.79±0.24，优于所有其他模型。影响疫苗反应的关键因素包括年龄、免疫抑制、突破性感染、BMI、性别和移植类型。这些发现表明，人工智能可以成为定制疫苗策略、改善弱势移植受者的健康结果的宝贵工具。