Does machine learning have a role in the prediction of asthma in children?

Patel, Dhwani A.; Hall, Graham; Broadhurst, David; Smith, Anne; Schultz, André; Foong, Rachel E.

doi:10.1016/j.prrv.2021.06.002

Cited by 16 publications

(23 citation statements)

References 52 publications

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“…This is consistent with Owora et al.‘s novel tree‐based model offering better predictive performance compared to an equivalent regression‐based PARS model (AUC = 0.85 vs. 0.71) 25 . Many of the other machine learning models also demonstrated greater performance to predict asthma than existing regression‐based models 26 . However, with low sample sizes and indications of overfitting in many of these studies, the lack of external validation renders it impossible to evaluate any superior performance offered by these models, especially since they were all developed in high‐risk populations.…”

Section: Discussionsupporting

confidence: 85%

“…), of which only six have been externally validated (Table E9). A recent systematic review further identified 10 studies that developed prediction models for childhood asthma using machine learning approaches, but only eight specifically predicted school‐age asthma (5–14 years) 26 . Another study directly compared the performance of a current regression‐based asthma prediction model, PARS, with a conditional inference tree‐based decision rule model using the same predictors 25 .…”

Section: Discussionmentioning

confidence: 99%

“…However, RFE identifies the subset of features that collectively offer the best predictive accuracy rather than devising a comprehensive list of childhood asthma risk factors, which may be biologically sound but lacking in predictive power 37 . In fact, the predictors of wheeze and cough were among those repeatedly included in the majority of machine learning models identified to date 26 . The predictors of atopy, polysensitisation and wheeze were also included in Owora et al.‘s machine learning model, however the predictors were taken from the PARS model rather than being identified from an independent feature selection 25 .…”

Section: Discussionmentioning

confidence: 99%

“…(5-14 years). 26 Another study directly compared the performance of a current regression-based asthma prediction model, PARS, with a conditional inference tree-based decision rule model using the same predictors. 25 However, none of these studies externally validated the machine learning models they proposed.…”

Section: Comparisons With Existing Modelsmentioning

confidence: 99%

“…While these studies tend to offer improved predictive performance, none of these studies support their findings with external validations of their models or explain how their “black‐box” models (where relevant) arrive at their predictions. Without these two components, machine learning models will fail to obtain the trust of physicians and continue to be limited in their clinical utility, regardless of the superior prediction accuracy they may offer 26,27 …”

Section: Introductionmentioning

confidence: 99%

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Development of childhood asthma prediction models using machine learning approaches

Kothalawala

Murray

Simpson

et al. 2021

Clinical & Translational All

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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.

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Section: Discussionsupporting

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Comparisons With Existing Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Development of childhood asthma prediction models using machine learning approaches

Kothalawala

Murray

Simpson

et al. 2021

Clinical & Translational All

View full text Add to dashboard Cite

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Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review

Darsha Jayamini,

Mirza,

Asif Naeem

et al. 2024

J Med Syst

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Asthma, a common chronic respiratory disease among children and adults, affects more than 200 million people worldwide and causes about 450,000 deaths each year. Machine learning is increasingly applied in healthcare to assist health practitioners in decision-making. In asthma management, machine learning excels in performing well-defined tasks, such as diagnosis, prediction, medication, and management. However, there remain uncertainties about how machine learning can be applied to predict asthma exacerbation. This study aimed to systematically review recent applications of machine learning techniques in predicting the risk of asthma attacks to assist asthma control and management. A total of 860 studies were initially identified from five databases. After the screening and full-text review, 20 studies were selected for inclusion in this review. The review considered recent studies published from January 2010 to February 2023. The 20 studies used machine learning techniques to support future asthma risk prediction by using various data sources such as clinical, medical, biological, and socio-demographic data sources, as well as environmental and meteorological data. While some studies considered prediction as a category, other studies predicted the probability of exacerbation. Only a group of studies applied prediction windows. The paper proposes a conceptual model to summarise how machine learning and available data sources can be leveraged to produce effective models for the early detection of asthma attacks. The review also generated a list of data sources that other researchers may use in similar work. Furthermore, we present opportunities for further research and the limitations of the preceding studies.

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Sociome Data Commons: A scalable and sustainable platform for investigating the full social context and determinants of health

Tilmon,

Nyenhuis,

Solomonides

et al. 2023

J. Clin. Trans. Sci.

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Background/Objective: Non-clinical aspects of life, such as social, environmental, behavioral, psychological, and economic factors, what we call the sociome, play significant roles in shaping patient health and health outcomes. This paper introduces the Sociome Data Commons (SDC), a new research platform that enables large-scale data analysis for investigating such factors. Methods: This platform focuses on “hyper-local” data, i.e., at the neighborhood or point level, a geospatial scale of data not adequately considered in existing tools and projects. We enumerate key insights gained regarding data quality standards, data governance, and organizational structure for long-term project sustainability. A pilot use case investigating sociome factors associated with asthma exacerbations in children residing on the South Side of Chicago used machine learning and six SDC datasets. Results: The pilot use case reveals one dominant spatial cluster for asthma exacerbations and important roles of housing conditions and cost, proximity to Superfund pollution sites, urban flooding, violent crime, lack of insurance, and a poverty index. Conclusion: The SDC has been purposefully designed to support and encourage extension of the platform into new data sets as well as the continued development, refinement, and adoption of standards for dataset quality, dataset inclusion, metadata annotation, and data access/governance. The asthma pilot has served as the first driver use case and demonstrates promise for future investigation into the sociome and clinical outcomes. Additional projects will be selected, in part for their ability to exercise and grow the capacity of the SDC to meet its ambitious goals.

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Does machine learning have a role in the prediction of asthma in children?

Cited by 16 publications

References 52 publications

Development of childhood asthma prediction models using machine learning approaches

Development of childhood asthma prediction models using machine learning approaches

Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review

Sociome Data Commons: A scalable and sustainable platform for investigating the full social context and determinants of health

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