人工智能用于使用大规模纵向身体组成数据进行儿科身高预测
Dohyun Chun1,2, Hae Woon Jung3, Jongho Kang2,4
1College of Business Administration, Kangwon National University, Chuncheon, Korea.
Digital health
|November 27, 2025
概括
我们创建了一个人工智能模型,使用人类测量数据预测孩子的未来身高. 该工具提供准确,个性化的生长曲线,有助于早期发现生长障碍.
科学领域:
- 儿科内分泌学和生长评估.
- 医疗保健中的人工智能.
- 用于人类发展的生物识别数据分析.
背景情况:
- 准确预测儿童和青少年身高对于监测成长和识别潜在疾病至关重要.
- 传统的增长评估方法可能缺乏精度和个性化.
- 人工智能的进步为儿科中复杂的预测建模提供了新的可能性.
研究的目的:
- 开发和验证精确的人工智能 (AI) 模型,用于预测儿童和青少年未来的身高.
- 为了利用人体测量和人体成分数据进行准确的生长轨迹估计.
- 增强儿科生长评估中的临床决策支持.
主要方法:
- 利用大规模的韩国纵向队列数据集 (96,485名儿童,588,546次测量).
- 开发了一种使用光梯度增强方法的预测模型,结合人体计量指标,身体组成,SDS和速度参数.
- 使用RMSE,MAE和MAPE评估模型性能;使用SHAP进行解释.
主要成果:
- 人工智能模型在预测男性和女性未来身高方面表现出高准确性 (RMSE < 2.51厘米).
- 确定的关键预测因素包括高度SDS,高度速度和软质速度.
- 通过估计个体身高轨迹和识别关键变量,生成个性化的增长曲线.
结论:
- 开发的AI模型提供了准确的,个性化的增长曲线与可解释的AI见解.
- 这种方法推进了儿科生长评估,并支持对生长障碍的临床决策.
- 该模型显示了早期识别和管理生长异常的巨大潜力.
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