Development of childhood asthma prediction models using machine learning approaches

Abstract

Background: Respiratory symptoms are common in early life and often transient. It is difficult to identify in which children these will persist and result in asthma.Machine learning (ML) approaches have the potential for better predictive performance and generalisability over existing childhood asthma prediction models. This study applied ML approaches to predict school-age asthma (age 10) in early life (Childhood Asthma Prediction in Early life, CAPE model) and at preschool age (Childhood Asthma Prediction at Preschool age, CAPP model).Methods: Clinical and environmental exposure data was collected from children enrolled in the Isle of Wight Birth Cohort (N = 1368, ∼15% asthma prevalence). Recursive Feature Elimination (RFE) identified an optimal subset of features predictive of school-age asthma for each model. Seven state-of-the-art ML classification algorithms were used to develop prognostic models. Training was performed by applying fivefold cross-validation, imputation, and resampling. Predictive performance was evaluated on the test set. Models were further externally validated in the Manchester Asthma and Allergy Study (MAAS) cohort. Results: RFE identified eight and twelve predictors for the CAPE and CAPP models, respectively. Support Vector Machine (SVM) algorithms provided the best performance for both the CAPE (area under the receiver operating characteristic curve, AUC = 0.71) and CAPP (AUC = 0.82) models. Both models demonstrated good generalisability in MAAS (CAPE 8-year = 0.71, 11-year = 0.71, CAPP 8-year = 0.83, 11-year = 0.79) and excellent sensitivity to predict a subgroup of persistent wheezers. Conclusion: Using ML approaches improved upon the predictive performance of existing regression-based models, with good generalisability and ability to rule in asthma and predict persistent wheeze. John W. Holloway and Faisal I. Rezwan contributed equally.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

A Methylation Diagnostic Model Based on Random Forests and Neural Networks for Asthma Identification

“…In addition, it facilitates the clinicians in patient management. Multiple studies have developed novel models to predict clinical outcomes of asthma [30][31][32]. In this study, we focused on the key role of epigenetics (methylation) in asthma.…”

Current state and prospects of artificial intelligence in allergy

“…[62][63][64] For example, SHAP was recently implemented to describe the contribution of features selected for inclusion in asthma prediction models. 65 These analytical methods calculate how each input feature contributes to each prediction, providing detailed insights into the learning patterns of the AI model.…”

“…The urgent need for explainability has accelerated methodological innovations to “open the black box.” Relevant examples are SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model‐agnostic Explanations), and CAM (Class Activation Maps) 62–64 . For example, SHAP was recently implemented to describe the contribution of features selected for inclusion in asthma prediction models 65 . These analytical methods calculate how each input feature contributes to each prediction, providing detailed insights into the learning patterns of the AI model.…”

“…The study was not independently replicated, which is essential in pursuing clinical support tools. Kothalawala et al 65 used data from birth cohorts to train and validate two predictive models, CAPE and CAPP, to predict the likelihood of asthma at school‐age using predictors from 0–2 to 0–4 years of age, respectively. Predictive performance was externally validated in the Manchester Asthma and Allergy Study (MAAS) cohort 122 .…”

Respiratory virus type to guide predictive enrichment approaches in the management of the first episode of bronchiolitis: A systematic review

“…On the other hand, increasing evidence demonstrates that bronchiolitis is a heterogeneous disease and that a viral trigger may be one of the key exposures and part of the underlying pathobiology, very important in identifying endotypes ( 2 , 10 , 11 ). Although the clinical features of bronchiolitis attributed to different viruses are usually indistinguishable, the recently recognized bronchiolitis profiles are associated with various risks for recurrent wheeze and asthma, some differences in disease severity, and, potentially, different therapeutic responses to systemic corticosteroids ( 10 – 13 ). For example, it has been shown that human rhinovirus (HRV)-associated bronchiolitis can result in shorter hospitalization times than bronchiolitis caused by the respiratory syncytial virus (RSV); however, the evidence around associations between virus type and severity is still unclear ( 14 , 15 ).…”