ArticleDigital health
Height estimation in children and adolescents using body composition big data: Machine-learning and explainable artificial intelligence approach.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Article
- Artificial intelligence for pediatric height prediction using large-scale longitudinal body composition data.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To develop an accurate and interpretable height estimation model for children and adolescents using body composition variables and explainable artificial intelligence approaches. Methods: A light gradient boosting method was employed on a dataset of 278,301 measurements from 54,374 children and adolescents aged 6-18 years. The model incorporated anthropometric and body composition measures. Model interpretability was enhanced through feature importance analysis, Shapley additive explanations, partial dependence plots, and accumulated local effects. Results: The models achieved high accuracy with mean absolute percentage errors of 1.64% and 1.63% for boys and girls, respectively. Soft lean mass (SLM), body fat mass percentage (BFMP), skeletal muscle mass, and skeletal muscle mass percentage were consistently identified as key factors influencing height estimation. Analysis revealed a positive correlation between SLM and estimated height, while BFMP exhibited an inverse relationship with height projections. Conclusion: These findings provide valuable insights into the relationship between body composition and height, underlining the potential of body composition variables as accurate height predictors in children and adolescents. The model's interpretability and accuracy make it a promising tool for pediatric growth assessment and monitoring.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.