Does machine learning have a role in the prediction of asthma in children?
Paediatr Respir Rev
; 41: 51-60, 2022 Mar.
Article
in En
| MEDLINE
| ID: mdl-34210588
ABSTRACT
Asthma is the most common chronic lung disease in childhood. There has been a significant worldwide effort to develop tools/methods to identify children's risk for asthma as early as possible for preventative and early management strategies. Unfortunately, most childhood asthma prediction tools using conventional statistical models have modest accuracy, sensitivity, and positive predictive value. Machine learning is an approach that may improve on conventional models by finding patterns and trends from large and complex datasets. Thus far, few studies have utilized machine learning to predict asthma in children. This review aims to critically assess these studies, describe their limitations, and discuss future directions to move from proof-of-concept to clinical application.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Asthma
/
Machine Learning
Type of study:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Child
/
Humans
Language:
En
Journal:
Paediatr Respir Rev
Journal subject:
PEDIATRIA
Year:
2022
Document type:
Article