ArticleDiagnostics (Basel, Switzerland)2021
Predictors of Newborn's Weight for Height: A Machine Learning Study Using Nationwide Multicenter Ultrasound Data.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Machine learning-based risk factor analysis of necrotizing enterocolitis in very low birth weight infants.Scientific reports · 2022Article
- Machine learning-based risk factor analysis of adverse birth outcomes in very low birth weight infants.Scientific reports · 2022Article
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Authors and funding
13 authors.
Funding
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Abstract
There has been no machine learning study with a rich collection of clinical, sonographic markers to compare the performance measures for a variety of newborns' weight-for-height indicators. This study compared the performance measures for a variety of newborns' weight-for-height indicators based on machine learning, ultrasonographic data and maternal/delivery information. The source of data for this study was a multi-center retrospective study with 2949 mother-newborn pairs. The mean-squared-error-over-variance measures of five machine learning approaches were compared for newborn's weight, newborn's weight/height, newborn's weight/height
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