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Implementation of prognostic machine learning algorithms in paediatric chronic respiratory conditions: a scoping review.

Filipow, Nicole; Main, Eleanor; Sebire, Neil J; Booth, John; Taylor, Andrew M; Davies, Gwyneth; Stanojevic, Sanja; (2022) Implementation of prognostic machine learning algorithms in paediatric chronic respiratory conditions: a scoping review. BMJ Open Respir Res , 9 (1) , Article e001165. 10.1136/bmjresp-2021-001165. Green open access

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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.

Type: Article
Title: Implementation of prognostic machine learning algorithms in paediatric chronic respiratory conditions: a scoping review.
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1136/bmjresp-2021-001165
Publisher version: http://dx.doi.org/10.1136/bmjresp-2021-001165
Language: English
Additional information: This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) licence.
Keywords: bronchiectasis, cystic fibrosis, paediatric asthma, paediatric lung disaese
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Childrens Cardiovascular Disease
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Population, Policy and Practice Dept
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Infection, Immunity and Inflammation Dept
URI: https://discovery.ucl.ac.uk/id/eprint/10145546
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