Machine learning algorithms to predict outcomes in children and adolescents with COVID-19: A systematic review.

Machine learning algorithms to predict outcomes in children and adolescents with COVID-19: A systematic review.

Publication date: Apr 01, 2024

We aimed to analyze the study designs, modeling approaches, and performance evaluation metrics in studies using machine learning techniques to develop clinical prediction models for children and adolescents with COVID-19. We searched four databases for articles published between 01/01/2020 and 10/25/2023, describing the development of multivariable prediction models using any machine learning technique for predicting several outcomes in children and adolescents who had COVID-19. We included ten articles, six (60 % [95 % confidence interval (CI) 0. 31 – 0. 83]) were predictive diagnostic models and four (40% [95 % CI 0. 170. 69]) were prognostic models. All models were developed to predict a binary outcome (n= 10/10, 100 % [95 % CI 0. 72-1]). The most frequently predicted outcome was disease detection (n=3/10, 30% [95 % CI 0. 11-0. 60]). The most commonly used machine learning models in the studies were tree-based (n=12/33, 36. 3% [95 % CI 0. 17-0. 47]) and neural networks (n=9/27, 33. 2% [95% CI 0. 15-0. 44]). Our review revealed that attention is required to address problems including small sample sizes, inconsistent reporting practices on data preparation, biases in data sources, lack of reporting metrics such as calibration and discrimination, hyperparameters and other aspects that allow reproducibility by other researchers and might improve the methodology.

Concepts Keywords
Covid Adolescents
Databases Aimed
Discrimination Algorithms
Analyze
Covid
Designs
Learning
Metrics
Models
Outcome
Outcomes
Predict
Reporting
Review
Systematic

Semantics

Type Source Name
disease MESH COVID-19
drug DRUGBANK Pentaerythritol tetranitrate
disease VO LACK

Original Article

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