开发一种基于机器学习的模型,用于在患有甲状腺功能障碍的青少年中调整甲基马的剂量
Joon Young Kim1, Kanghyuck Lee2, Eunsik Choi3
1Department of Pediatrics, Yonsei University college of Medicine, Gangnam Severance Hospital, Eonju-ro, Gangnam-gu, Seoul, 06273, Republic of Korea.
The Journal of clinical endocrinology and metabolism
|October 8, 2025
概括
机器学习模型现在可以预测儿科甲状腺功能增强症的最佳甲基马 (MMI) 剂量,提高临床效率. XGBoost模型在预测儿童MMI剂量方面表现最好.
科学领域:
- 内分泌学 在内分泌学.
- 儿科医学 儿科医学
- 计算生物学 计算生物学
背景情况:
- 准确的甲基马 (MMI) 剂量对于儿科甲状腺功能障碍症至关重要,但由于缺乏预测工具,目前的定位依赖于临床专业知识.
- 个性化的MMI剂量需要经过验证的方法来优化儿童的治疗结果.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测儿科甲状腺功能障碍症中最佳的MMI剂量.
- 在年轻患者中建立基于数据的MMI剂量调整方法.
主要方法:
- 一项涉及ML模型 (线性回归,决策树,支持向量的回归,XGBoost,前神经网络) 的回顾性多中心研究.
- 模型以1512次访问的数据进行训练,并使用两个外部队列 (666次和31次访问) 进行验证.
- 预测因素包括年龄,性别,人体测量,先前的MMI剂量,治疗持续时间和甲状腺功能测试. 性能以平均绝对误差 (MAE) 来衡量.
主要成果:
- 极端梯度增强 (XGBoost) 模型以1.72 mg的MAE (内部验证) 和1.08 mg (外部验证) 实现了最佳性能.
- 沙普利添加剂解释 (SHAP) 分析确定了以前的MMI剂量,三甲状腺素和自由甲状腺素水平作为关键预测因素.
结论:
- 这项研究提出了第一个数据驱动的工具,用于指导小儿甲状腺功能障碍症中甲基马的剂量.
- 开发的ML模型可以提高临床效率,并支持为儿童个性化MMI定位.
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