Implementation of prognostic machine learning algorithms in paediatric chronic respiratory conditions: a scoping review. Issue 1 (16th March 2022)

• Record Type: Journal Article
• Authors: Filipow, Nicole; Main, Eleanor; Sebire, Neil J; Booth, John; Taylor, Andrew M; Davies, Gwyneth; Stanojevic, Sanja
• Is Part Of: BMJ open respiratory research. Volume 9:Issue 1(2022)
• Publication Date: 2022-03-16
• Subjects: bronchiectasis -- cystic fibrosis -- paediatric asthma -- paediatric lung disaese; Respiratory organs -- Diseases -- Periodicals; Respiratory organs -- Diseases -- Treatment -- Periodicals; Respiratory therapy -- Periodicals; 616.2005
• DOI: 10.1136/bmjresp-2021-001165
• Languages: English
• ISSNs: 2052-4439

Abstract

Abstract : Machine learning (ML) holds great potential for predicting clinical outcomes in heterogeneous chronic respiratory diseases (CRD) affecting children, where timely individualised treatments offer opportunities for health optimisation. This paper identifies rate-limiting steps in ML prediction model development that impair clinical translation and discusses regulatory, clinical and ethical considerations for ML implementation. A scoping review of ML prediction models in paediatric CRDs was undertaken using the PRISMA extension scoping review guidelines. From 1209 results, 25 articles published between 2013 and 2021 were evaluated for features of a good clinical prediction model using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines. Most of the studies were in asthma (80%), with few in cystic fibrosis (12%), bronchiolitis (4%) and childhood wheeze (4%). There were inconsistencies in model reporting and studies were limited by a lack of validation, and absence of equations or code for replication. Clinician involvement during ML model development is essential and diversity, equity and inclusion should be assessed at each step of the ML pipeline to ensure algorithms do not promote or amplify health disparities among marginalised groups. As ML prediction studies become more frequent, it is important that models are rigorously developed using published guidelines and take account of regulatory frameworks which depend on model complexity, patient safety, accountability and liability.