用大样本数据预测儿童身高，准确率超99%
Artificial Intelligence for Pediatric Height Prediction Using Large-Scale Longitudinal Body Composition Data
- 融合体测与体成分数据，用AI预测未来身高
- 男性平均误差2.51厘米，女性2.28厘米，误差极小
- 可生成个性化生长曲线，适合临床早筛生长异常
本研究基于GP队列研究（96,485名7-18岁儿童，共588,546次测量）的体测与体成分数据，构建了高精度人工智能模型以预测儿童及青少年未来身高。模型整合了身高标准差分数（height SDS）、生长速度、软瘦体重变化等参数。评估结果显示：男性平均均方根误差（RMSE）、平均绝对误差（MAE）和平均绝对百分比误差（MAPE）分别为2.51厘米、1.74厘米和1.14%；女性分别为2.28厘米、1.68厘米和1.13%。可解释AI方法识别出高度标准差分数、身高增速和软瘦体重增速为关键预测因子。模型可生成个体化生长轨迹，为临床决策支持、生长障碍早期识别及生长结果优化提供有力工具。
原文摘要 · Abstract (English)
This study developed an accurate artificial intelligence model for predicting future height in children and adolescents using anthropometric and body composition data from the GP Cohort Study (588,546 measurements from 96,485 children aged 7-18). The model incorporated anthropometric measures, body composition, standard deviation scores, and growth velocity parameters, with performance evaluated using RMSE, MAE, and MAPE. Results showed high accuracy with males achieving average RMSE, MAE, and MAPE of 2.51 cm, 1.74 cm, and 1.14%, and females showing 2.28 cm, 1.68 cm, and 1.13%, respectively. Explainable AI approaches identified height SDS, height velocity, and soft lean mass velocity as crucial predictors. The model generated personalized growth curves by estimating individual-specific height trajectories, offering a robust tool for clinical decision support, early identification of growth disorders, and optimization of growth outcomes.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。