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Predicting the therapeutic efficacy of AIT for asthma using clinical characteristics, serum allergen detection
Hao Yao1, Lingya Wang1, Xinyu Zhou1
1Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China.
Insights
This study developed a machine learning model to predict the effectiveness of mite allergen immunotherapy (AIT) for childhood asthma. The model accurately identifies key indicators for successful treatment outcomes.
Area of Science:
- Allergy and Immunology
- Computational Biology
- Pediatric Pulmonology
Background:
- Childhood asthma is a common respiratory condition.
- Allergen immunotherapy (AIT) is a treatment option for allergic asthma.
- Predicting AIT efficacy is crucial for personalized treatment.
Purpose of the Study:
- To develop a predictive framework for mite subcutaneous immunotherapy efficacy in children with asthma using machine learning.
- To introduce an improved Salp Swarm Algorithm (DFSSA) for enhanced optimization.
- To establish a robust prediction model for AIT performance.
Main Methods:
- Collected clinical data from 390 children (aged 4-17) with asthma undergoing AIT.
- Developed a dispersed foraging strategy Salp Swarm Algorithm (DFSSA).
- Fused DFSSA with a kernel extreme learning machine (KELM) to create the DFSSA-KELM prediction model.
Main Results:
- The DFSSA-KELM model achieved 87.18% accuracy and 93.55% sensitivity in predicting AIT efficacy.
- Key predictive indicators identified include blood basophil count and specific IgE levels (Der p, Der f).
- The model demonstrated effectiveness as an intelligent tool for evaluating AIT outcomes.
Conclusions:
- Machine learning, particularly the DFSSA-KELM model, offers an accurate method for predicting mite AIT efficacy in pediatric asthma.
- Specific biomarkers are vital for forecasting treatment success.
- This predictive framework can aid clinicians in optimizing asthma management strategies.
Abstract:
Bronchial asthma is a prevalent non-communicable disease among children. The study collected clinical data from 390 children aged 4-17 years with asthma, with or without rhinitis, who received allergen immunotherapy (AIT). Combining these data, this paper proposed a predictive framework for the efficacy of mite subcutaneous immunotherapy in asthma based on machine learning techniques. Introducing the dispersed foraging strategy into the Salp Swarm Algorithm (SSA), a new improved algorithm named DFSSA is proposed. This algorithm effectively alleviates the imbalance between search speed and traversal caused by the fixed partitioning pattern in traditional SSA. Utilizing the fusion of boosting algorithm and kernel extreme learning machine, an AIT performance prediction model was established. To further investigate the effectiveness of the DFSSA-KELM model, this study conducted an auxiliary diagnostic experiment using the immunotherapy predictive medical data collected by the hospital. The findings indicate that selected indicators, such as blood basophil count, sIgE/tIgE (Der p) and sIgE/tIgE (Der f), play a crucial role in predicting treatment outcome. The classification results showed an accuracy of 87.18% and a sensitivity of 93.55%, indicating that the prediction model is an effective and accurate intelligent tool for evaluating the efficacy of AIT.
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