Predicting the therapeutic efficacy of AIT for asthma using clinical characteristics, serum allergen detection metrics, and machine learning techniques

Yao, Hao; Wang, Lingya; Zhou, Xinyu; Jia, Xiaoxiao; Xiang, Qiangwei; Zhang, Weixi

doi:10.1016/j.compbiomed.2023.107544

Cited by 2 publications

(1 citation statement)

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“…The ensemble learning model in the prediction of adherence from the patients who conducted self-administer injections proposed by Gu et al (2021) achieved a good performance and generalization properties based on the fusion of multiple heterogeneous classifiers. In the field of allergen immunotherapy, Yao et al (2023) introduced a machine-learning model with an improved DFSSA algorithm to predict the therapeutic efficacy of AIT for asthma using clinical characteristics and serum allergen detection metrics. However, these non-sequential methods generally predict only the final outcome, neglecting the complexities of intermediate stages.…”

Section: Discussionmentioning

confidence: 99%

Sequential model for predicting patient adherence in subcutaneous immunotherapy for allergic rhinitis

Li,

Xiong,

Fan

et al. 2024

Front. Pharmacol.

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ObjectiveSubcutaneous 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 3 years SCIT.MethodsThe research develops and analyzes two models, sequential latent-variable model (SLVM) of Stochastic Latent Actor-Critic (SLAC) and Long Short-Term Memory (LSTM). SLVM is a probabilistic model that captures the dynamics of patient adherence, while LSTM is a type of recurrent neural network designed to handle time-series data by maintaining long-term dependencies. These models were evaluated based on scoring and adherence prediction capabilities.ResultsExcluding 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%–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.ConclusionWe 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.

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Section: Discussionmentioning

confidence: 99%