  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 Stochastic 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.
