Time Series for Patient Adherence

Subcutaneous Immunotherapy (SCIT) is the long-lasting causal treatment of allergic rhinitis (AR). How to predict and enhance the adherence of patients to maximize the benefit of allergen immunotherapy (AIT) plays a crucial role in improving the efficiency of AIT management. To address this challenge, this study explores the application of the sequential model of Stochastic Latent Actor-Critic (SLAC) and Long Short-Term Memory (LSTM) models in predicting patient adherence and symptom scores in AIT for allergic rhinitis. By developing and analyzing these models, we creatively apply sequential models in the long-term management of SCIT with promising accuracy in the prediction of SCIT adherence in AR patients.