Machine learning did not beat logistic regression in time series prediction for severe asthma exacerbations
Scientific Reports, vol. 12, pp. 20363
Abstract
Early detection of severe asthma exacerbations through home monitoring data in patients with stable mild-to-moderate chronic asthma could help to timely adjust medication. We evaluated the potential of machine learning methods compared to a clinical rule and logistic regression to predict severe exacerbations. We used daily home monitoring data from two studies in asthma patients (development: n = 165 and validation: n = 101 patients). Two ML models (XGBoost, one class SVM) and a logistic regression model provided predictions based on peak expiratory flow and asthma symptoms. These models were compared with an asthma action plan rule. Severe exacerbations occurred in 0.2% of all daily measurements in the development (154/92,787 days) and validation cohorts (94/40,185 days). The AUC of the best performing XGBoost was 0.85 (0.82-0.87) and 0.88 (0.86-0.90) for logistic regression in the validation cohort. The XGBoost model provided overly extreme risk estimates, whereas the logistic regression underestimated predicted risks. Sensitivity and specificity were better overall for XGBoost and logistic regression compared to one class SVM and the clinical rule. We conclude that ML models did not beat logistic regression in predicting short-term severe asthma exacerbations based on home monitoring data. Clinical application remains challenging in settings with low event incidence and high false alarm rates with high sensitivity.
Authors 6
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Anne de Hond corresponding Aachen Clinical AI Implementation and Research Lab Department of Biomedical Data Sciences Department of Information Technology and Digital Innovation
Leiden University · Leiden University Medical Center
Affiliation as printed
Clinical AI Implementation and Research Lab, Leiden University Medical Centre, Albinusdreef 2, 2300 RC, Leiden, The Netherlands. a.a.h.de_hond@lumc.nl
Department of Biomedical Data Sciences, Leiden University Medical Centre, Albinusdreef 2, 2300 RC, Leiden, the Netherlands. a.a.h.de_hond@lumc.nl
Department of Information Technology and Digital Innovation, Leiden University Medical Centre, Albinusdreef 2, 2300 RC, Leiden, The Netherlands. a.a.h.de_hond@lumc.nl
Department of Information Technology and Digital Innovation, Leiden University Medical Centre, Albinusdreef 2, 2300 RC, Leiden, The Netherlands
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Ilse M.J. Kant Aachen Clinical AI Implementation and Research Lab Department of Biomedical Data Sciences Department of Information Technology and Digital Innovation
Leiden University · Leiden University Medical Center
Affiliation as printed
Clinical AI Implementation and Research Lab, Leiden University Medical Centre, Albinusdreef 2, 2300 RC, Leiden, The Netherlands
Department of Biomedical Data Sciences, Leiden University Medical Centre, Albinusdreef 2, 2300 RC, Leiden, the Netherlands
Department of Information Technology and Digital Innovation, Leiden University Medical Centre, Albinusdreef 2, 2300 RC, Leiden, The Netherlands
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Leiden University · Leiden University Medical Center
Affiliation as printed
Department of Biomedical Data Sciences, Leiden University Medical Centre, Albinusdreef 2, 2300 RC, Leiden, the Netherlands
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Affiliation as printed
Department of Respiratory Medicine, University Hospital Wishaw, 50 Netherton Street, Wishaw, ML2 0DP, UK
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Ewout Willem Steyerberg Aachen Clinical AI Implementation and Research Lab Department of Biomedical Data Sciences
Leiden University · Leiden University Medical Center
Affiliation as printed
Clinical AI Implementation and Research Lab, Leiden University Medical Centre, Albinusdreef 2, 2300 RC, Leiden, The Netherlands
Department of Biomedical Data Sciences, Leiden University Medical Centre, Albinusdreef 2, 2300 RC, Leiden, the Netherlands
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Leiden University · Leiden University Medical Center
Affiliation as printed
Department of Biomedical Data Sciences, Leiden University Medical Centre, Albinusdreef 2, 2300 RC, Leiden, the Netherlands
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