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...

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Auteurs principaux: 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
Langue:EN
Publié: Wiley 2021
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Accès en ligne:https://doaj.org/article/eaa4da07fb3e4441aacd4bf9bf76eee0
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