Detecting asthma exacerbations using daily home monitoring and machine learning.

Journal: The Journal of asthma : official journal of the Association for the Care of Asthma

PMID: 32718193

Abstract

OBJECTIVE: Acute exacerbations contribute significantly to the morbidity of asthma. Recent studies have shown that early detection and treatment of asthma exacerbations leads to improved outcomes. We aimed to develop a machine learning algorithm to detect severe asthma exacerbations using easily available daily monitoring data.

Authors

Olivier Zhang

Department of Computer Engineering (INFO), INSA Rennes, Rennes, France.
Leandro L Minku

School of Computer Science, University of Birmingham, UK.
Sherif Gonem

Dept of Respiratory Medicine, Nottingham University Hospitals NHS Trust, Nottingham, UK.

Keywords

Asthma Bronchodilator Agents Budesonide Disease Progression Drug Combinations Female Formoterol Fumarate Home Care Services Humans Machine Learning Male Middle Aged Monitoring, Physiologic Severity of Illness Index Terbutaline

External Resources

View on PubMed Access via DOI PubMed (32718193)

Detecting asthma exacerbations using daily home monitoring and machine learning.

Abstract

Authors

Keywords

External Resources

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