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Predicting Treatment Outcomes Using Explainable Machine Learning in Children with Asthma.

Mario Lovrić1, Ivana Banić2, Emanuel Lacić1

  • 1Knowledge Discovery, Know-Center, Infeldgasse 13, 8010 Graz, Austria.

Children (Basel, Switzerland)
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Summary

Machine learning accurately predicts pediatric asthma treatment success, particularly for symptom control and fractional exhaled nitric oxide (FENO) levels. Key predictors include asthma severity and immunoglobulin E (IgE), aiding precision medicine approaches.

Keywords:
asthma controlasthma controller medicationchildhood asthmamachine learningtreatment outcome

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Area of Science:

  • Pediatric Pulmonology
  • Computational Biology
  • Immunology

Background:

  • Childhood asthma is a complex, heterogeneous disease with variable treatment responses.
  • Current anti-inflammatory treatments are inadequate for a significant number of pediatric asthma patients.
  • Understanding predictors of treatment success is crucial for optimizing management strategies.

Purpose of the Study:

  • To employ machine learning algorithms for predicting treatment success in pediatric asthma.
  • To identify key variables influencing treatment outcomes for improved mechanistic understanding.
  • To evaluate prediction accuracy based on asthma control, lung function (FEV1, MEF50), and fractional exhaled nitric oxide (FENO).

Main Methods:

  • Utilized Random Forest and AdaBoost classifiers on a cohort of 365 children with mild to severe asthma.
  • Assessed treatment outcomes after 6 months of controller medication based on changes in asthma control, FEV1, MEF50, and FENO.
  • Identified predictive variables associated with treatment response.

Main Results:

  • Machine learning models achieved higher prediction power for asthma control and FENO-based outcomes, especially in younger children.
  • Asthma severity and total IgE were significant predictors for both asthma control and FENO outcomes.
  • MEF50-based outcomes were better predicted than FEV1-based responses, with hsCRP highlighting distal airway involvement.

Conclusions:

  • Machine learning offers a robust tool for predicting treatment success in pediatric asthma, outperforming traditional lung function metrics.
  • Asthma control and FENO-guided management, complemented by machine learning predictions, support precision medicine in pediatric asthma.
  • T2-high asthma phenotype appears to respond best to anti-inflammatory therapies, warranting further investigation.