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

Authors
  • Lovrić, Mario1
  • Banić, Ivana2
  • Lacić, Emanuel1
  • Pavlović, Kristina1
  • Kern, Roman1, 3
  • Turkalj, Mirjana2, 4, 5
  • 1 Knowledge Discovery, Know-Center, Infeldgasse 13, 8010 Graz, Austria. , (Austria)
  • 2 Srebrnjak Children's Hospital, Srebrnjak 100, 10000 Zagreb, Croatia. , (Croatia)
  • 3 Institute of Interactive Systems and Data Science, Graz University of Technology, Inffeldgasse 16C, 8010 Graz, Austria. , (Austria)
  • 4 Faculty of Medicine, J.J. Strossmayer University of Osijek, Josipa Huttlera 4, 31000 Osijek, Croatia. , (Croatia)
  • 5 Medical School, Catholic University of Croatia, Ilica 242, 10000 Zagreb, Croatia. , (Croatia)
Type
Published Article
Journal
Children
Publisher
MDPI AG
Publication Date
May 10, 2021
Volume
8
Issue
5
Identifiers
DOI: 10.3390/children8050376
PMID: 34068718
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Asthma in children is a heterogeneous disease manifested by various phenotypes and endotypes. The level of disease control, as well as the effectiveness of anti-inflammatory treatment, is variable and inadequate in a significant portion of patients. By applying machine learning algorithms, we aimed to predict the treatment success in a pediatric asthma cohort and to identify the key variables for understanding the underlying mechanisms. We predicted the treatment outcomes in children with mild to severe asthma (N = 365), according to changes in asthma control, lung function (FEV1 and MEF50) and FENO values after 6 months of controller medication use, using Random Forest and AdaBoost classifiers. The highest prediction power is achieved for control- and, to a lower extent, for FENO-related treatment outcomes, especially in younger children. The most predictive variables for asthma control are related to asthma severity and the total IgE, which were also predictive for FENO-based outcomes. MEF50-related treatment outcomes were better predicted than the FEV1-based response, and one of the best predictive variables for this response was hsCRP, emphasizing the involvement of the distal airways in childhood asthma. Our results suggest that asthma control- and FENO-based outcomes can be more accurately predicted using machine learning than the outcomes according to FEV1 and MEF50. This supports the symptom control-based asthma management approach and its complementary FENO-guided tool in children. T2-high asthma seemed to respond best to the anti-inflammatory treatment. The results of this study in predicting the treatment success will help to enable treatment optimization and to implement the concept of precision medicine in pediatric asthma treatment.

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