Implementation of prognostic machine learning algorithms in paediatric chronic respiratory conditions: a scoping review.
Implementation of prognostic machine learning algorithms in paediatric chronic respiratory conditions: a scoping review.
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DOI：
10.1136/bmjresp-2021-001165
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发表时间：
2022-03
影响因子：
4.1
通讯作者：
Stanojevic S
中科院分区：
文献类型：
--
作者：
Filipow N;Main E;Sebire NJ;Booth J;Taylor AM;Davies G;Stanojevic S
机器学习（ML）在预测影响儿童的异质性慢性呼吸道疾病（CRD）的临床结果方面具有巨大潜力，及时的个性化治疗为健康优化提供了机会。本文确定了ML预测模型开发中影响临床翻译的限速步骤，并讨论了ML实施的监管、临床和伦理考虑。使用PRISMA扩展范围审查指南对儿科CRD中的ML预测模型进行了范围审查。从1209个结果中，使用个体预后或诊断多变量预测模型的透明报告（TRIPOD）指南，对2013年至2021年期间发表的25篇文章进行了良好临床预测模型特征的评价。大多数研究涉及哮喘（80%），很少涉及囊性纤维化（12%）、细支气管炎（4%）和儿童喘息（4%）。模型报告中存在不一致之处，研究因缺乏验证以及缺乏用于复制的方程式或代码而受到限制。临床医生在ML模型开发过程中的参与至关重要，在ML管道的每一步都应评估多样性，公平性和包容性，以确保算法不会促进或扩大边缘化群体之间的健康差距。随着ML预测研究变得越来越频繁，重要的是使用已发布的指南严格开发模型，并考虑依赖于模型复杂性，患者安全性，问责制和责任的监管框架。
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.
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DOI：
10.1148/rg.2017160130
发表时间：
2017-03
期刊：
Radiographics : a review publication of the Radiological Society of North America, Inc
影响因子：
--
作者：
Erickson BJ;Korfiatis P;Akkus Z;Kline TL
通讯作者：
Kline TL
影响因子：
24.3
作者：
Filipow, Nicole;Davies, Gwyneth;Stanojevic, Sanja
通讯作者：
Stanojevic, Sanja
影响因子：
6.7
作者：
Bica, Ioana;Alaa, Ahmed M.;van der Schaar, Mihaela
通讯作者：
van der Schaar, Mihaela
影响因子：
3.1
作者：
Das, Lala T.;Abramson, Erika L.;Grinspan, Zachary M.
通讯作者：
Grinspan, Zachary M.
影响因子：
3.1
作者：
Hogan, Alexander H.;Brimacombe, Michael;Flores, Glenn
通讯作者：
Flores, Glenn