使用人工智能开发的预测模型:类似的预测性能与个人高度不同的预测 - - 深静脉血栓塞的说明
Maerziya Yusufujiang1, Constanza L Andaur Navarro2, Johanna Aa Damen2
1Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands. y.maerziya@umcutrecht.nl.
Diagnostic and prognostic research
|January 8, 2026
概括
用于深静脉血栓塞 (DVT) 预测的机器学习模型可以显示类似的性能,但产生不同的患者风险估计. 选择正确的模型会影响临床决策,即使有可比性歧视.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 临床预测建模临床预测建模
背景情况:
- 机器学习 (ML) 和人工智能提供了众多预测模型,为开发人员带来了选择.
- 模型之间的相似的区分性能 (例如,AUC) 可能表明可互换性.
- 然而,不同的结果概率估计可能会影响临床决策.
研究的目的:
- 为了比较深静脉栓塞 (DVT) 的诊断预测模型.
- 评估不同建模技术对个体风险预测的影响.
- 评估模型选择如何影响临床决策.
主要方法:
- 开发了五个用于DVT的诊断预测模型,使用后勤回归,随机森林,SVM和神经网络.
- 利用了6087名怀疑患有DVT的患者的数据集.
- 基于个人风险预测的歧视,校准和稳定性的评估模型.
主要成果:
- 三个模型 (ULR,RLR,NN) 显示了类似的歧视 (AUC ~0.84).
- 个人DVT概率估计在五种技术中差异很大.
- 随机森林高估了风险,SVM低估了风险;差异影响了接近值的分类措施.
结论:
- 不同的建模技术产生不同的个人结果概率,尽管具有相似的歧视性表现.
- 变化源于校准差异和固有的建模选择.
- 模型选择极大地影响个人风险估计和随后的临床决策.
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