Title:Stepping Up the Personalized Approach in COPD with Machine Learning
Volume: 19
Issue: 3
Author(s): Evgeni Mekov*, Marc Miravitlles, Marko Topalovic, Aran Singanayagam and Rosen Petkov
Affiliation:
- Department of Occupational Diseases, Medical Faculty, Medical University of Sofia, Sofia, Bulgaria
Keywords:
Machine learning, COPD, GOLD, Mortality, Exacerbations, Prediction.
Abstract:
Introduction: There is increasing interest in the application of artificial intelligence (AI)
and machine learning (ML) in all fields of medicine to facilitate greater personalisation of
management.
Methods: ML could be the next step of personalized medicine in chronic obstructive pulmonary
disease (COPD) by giving the exact risk (risk for exacerbation, death, etc.) of every patient (based
on his/her parameters like lung function, clinical data, demographics, previous exacerbations, etc.),
thus providing a prognosis/risk for the specific patient based on individual characteristics (individual
approach).
Result: ML algorithm might utilise some traditional risk factors along with some others that may be
location-specific (e.g. the risk of exacerbation thatmay be related to ambient pollution but that could
vary massively between different countries, or between different regions of a particular country).
Conclusion: This is a step forward from the commonly used assignment of patients to a specific
group for which prognosis/risk data are available (group approach).
