Development of childhood asthma prediction models using machine learning approaches
Abstract Background Respiratory symptoms are common in early life and often transient. It is difficult to identify in which children these will persist and result in asthma. Machine learning (ML) approaches have the potential for better predictive performance and generalisability over existing child...
Saved in:
| Main Authors: | Dilini M. Kothalawala, Clare S. Murray, Angela Simpson, Adnan Custovic, William J. Tapper, S. Hasan Arshad, John W. Holloway, Faisal I. Rezwan, STELAR/UNICORN investigators |
|---|---|
| Format: | article |
| Language: | EN |
| Published: |
Wiley
2021
|
| Subjects: | |
| Online Access: | https://doaj.org/article/eaa4da07fb3e4441aacd4bf9bf76eee0 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Altered Pattern of Macrophage Polarization as a Biomarker for Severity of Childhood Asthma
by: Kuo CH, et al.
Published: (2021) -
Childhood Asthma – The Effect of Asthma Specialist Intervention on Asthma Control: A Retrospective Review
by: Rosman Y, et al.
Published: (2021) -
Smarte Kindheiten
by: Dana Ghafoor-Zadeh, et al.
Published: (2021) -
Development and equivalence of new faces for inclusion in the Childhood Asthma Control Test (C-ACT) response scale
by: Kate Sully, et al.
Published: (2021) -
The Journal of asthma
Published: (1981)