通过跨医院验证,探索机器学习来预测外围和中心早熟青春期.
Chun-Yen Cheng1, Yung-Chun Chang2, Nguyen Quoc Khanh Le3
1Ph.D. Program in Medical Biotechnology, Taipei Medical University, Taipei, Taiwan.
Studies in health technology and informatics
|August 8, 2025
概括
机器学习模型可以预测早期青春期 (PP). 在外部验证中,随机森林模型在区分外围早期青春期 (PPP) 和中央早期青春期 (CPP) 中表现最好.
科学领域:
- 儿科内分泌学 儿科内分泌学
- 医疗保健中的机器学习
- 临床决策支持系统 临床决策支持系统
背景情况:
- 早期青春期 (PP),包括外围早期青春期 (PPP) 和中央早期青春期 (CPP),在儿科内分泌学中提出了诊断挑战.
- 迟到的PP诊断可能导致治疗结果低于最佳.
研究的目的:
- 开发和验证用于预测和区分PPP和CPP的机器学习模型.
- 评估不同机器学习模型在不同数据集中的通用性.
主要方法:
- 使用随机森林 (RF),梯度增强机 (GBM) 和极端梯度增强 (XGB) 模型.
- 从电子医疗记录 (EMR) 中提取了12个临床特征,用于模型培训和验证.
- 对TMUH数据进行内部验证,对WFH数据进行外部验证.
主要成果:
- 在内部验证中,XGB获得了最高的灵敏度 (0.88) 和AUC (0.86).
- 在外部验证中,RF表现出优越的概括性,灵敏度为0.91和AUC为0.89.
- 对于跨医院实施,RF显示出强度.
结论:
- 机器学习模型显示了改善早期青春期早期诊断的巨大潜力.
- 随机森林模型是预测和区分PPP和CPP在现实世界的临床环境中的一个强大的选择.
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