基于机器学习的预测模型,用于二次性副甲状腺功能障碍症的副甲状腺切除术后饥饿骨综合征
Yalin Chai1, Nan Yuan1, Jiaming Yin1
1The Affiliated Hospital of Qingdao University, Qingdao, China.
Frontiers in endocrinology
|September 22, 2025
概括
一个可解释的机器学习模型准确地预测了二次性甲状腺功能障碍症 (SHPT) 患者的饥饿骨综合征 (HBS) 风险. 关键预测因素包括手术前副甲状腺激素水平和患者年龄,有助于术后监测.
科学领域:
- 内分泌学 在内分泌学.
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 二次性偏甲状腺功能障碍症 (SHPT) 通常需要偏甲状腺切除术.
- 饥饿骨综合征 (HBS) 是对甲状腺切除术后的一种潜在并发症.
- 准确预测HBS风险对于患者管理至关重要.
研究的目的:
- 开发一种可解释的机器学习模型,用于预测副甲状腺切除术后SHPT患者的HBS风险.
- 确定 HBS 在偏甲状腺切除术后的关键预测因素.
- 提供一个工具来估计HBS风险.
主要方法:
- 对181名接受副甲状腺切除术的SHPT患者的回顾性分析.
- 利用后勤回归和Boruta算法从46个变量中选择了5个关键预测因素.
- 训练和评估了7个机器学习模型,包括XGBoost,使用ROC曲线,校准曲线和DCA.
- 使用夏普利添加式解释 (SHAP) 的量化模型解释性.
主要成果:
- 在验证队列中,XGBoost模型实现了0.878的AUC和0.871的F1得分.
- 确定了关键预测因素:手术前甲状腺激素 (Pre-PTH),%PTH衰变,性酸酶,血清和年龄.
- 开发了一个用于HBS风险估计的Web应用程序.
结论:
- 一个可解释的机器学习模型有效地预测了SHPT患者在副甲状腺切除术后的HBS风险.
- 该模型有助于指导术后监测策略.
- 早期识别高风险患者可以改善临床结果.
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