Objective: Subcutaneous Immunotherapy (SCIT) is the long-lasting causal treatment of allergic rhinitis (AR). How to enhance the adherence of patients to maximize the benefit of allergen immunotherapy (AIT) plays a crucial role in the management of AIT. This study aims to leverage novel machine learning models to precisely predict the risk of non-adherence of AR patients and related local symptom scores in three years SCIT. Methods: The research develops and analyzes two models, sequential latent-variable model (SLVM) of Sequential Latent Actor-Critic (SLAC) and Long Short-Term Memory (LSTM) evaluating them based on scoring and adherence prediction capabilities. Results: Excluding the biased samples at the first time step, the predictive adherence accuracy of the SLAC models is from 60\% to 72\%, and for LSTM models, it is 66\% to 84\%, varying according to the time steps. The range of Root Mean Square Error (RMSE) for SLAC models is between 0.93 and 2.22, while for LSTM models it is between 1.09 and 1.77. Notably, these RMSEs are significantly lower than the random prediction error of 4.55. Conclusion: We creatively apply sequential models in the long-term management of SCIT with promising accuracy in the prediction of SCIT nonadherence in AR patients. While LSTM outperforms SLAC in adherence prediction, SLAC excels in score prediction for patients undergoing SCIT for AR. The state-action-based SLAC adds flexibility, presenting a novel and effective approach for managing long-term AIT.

翻译：目的：皮下免疫疗法（SCIT）是变应性鼻炎（AR）的长期病因治疗方法。如何提高患者依从性以最大化变应原免疫疗法（AIT）的获益，在AIT管理中至关重要。本研究旨在利用新型机器学习模型，精准预测AR患者在三年SCIT期间的非依从性风险及相关局部症状评分。方法：本研究开发并分析了两种模型——序列潜在演员-评论家（SLAC）的序列潜变量模型（SLVM）和长短期记忆（LSTM）模型，基于评分和依从性预测能力对它们进行评估。结果：排除第一个时间步的偏差样本后，SLAC模型的依从性预测准确率在60%至72%之间，而LSTM模型的准确率在66%至84%之间，具体数值依时间步长而变化。SLAC模型的均方根误差（RMSE）范围在0.93至2.22之间，而LSTM模型的RMSE范围在1.09至1.77之间。值得注意的是，这些RMSE值均显著低于随机预测误差4.55。结论：我们创新性地将序列模型应用于SCIT的长期管理，在预测AR患者的SCIT非依从性方面展现出良好的准确性。虽然LSTM在依从性预测上优于SLAC，但SLAC在预测接受SCIT的AR患者评分方面表现更佳。基于状态-动作的SLAC模型增加了灵活性，为管理长期AIT提供了一种新颖且有效的方法。