在垂体腺瘤手术后,术后低血的预测建模
Guoge Li1, Xiaotong Li1, Yaowei Ding1
1Department of Clinical Diagnosis, Laboratory of Beijing Tiantan Hospital, Capital Medical University, Beijing, China; NMPA Key Laboratory for Quality Control of In Vitro Diagnostics, Beijing, China; Beijing Engineering Research Center of Immunological Reagents Clinical Research, Beijing, China.
Clinical neurology and neurosurgery
|June 27, 2025
概括
机器学习模型可以预测下垂体手术后的术后低血症. 随机森林模型表现出高准确度,识别出改善患者结果的关键预测因素.
科学领域:
- 内分泌学 在内分泌学.
- 神经外科 神经外科
- 医疗信息学 医疗信息学
背景情况:
- 手术后的低血是下垂体手术后的一个常见并发症.
- 准确预测低血症对于及时干预和改善患者结果至关重要.
研究的目的:
- 为了比较六个机器学习 (ML) 模型在下垂体手术后术后低血症的预测性能.
- 确定影响低血症发展的关键临床和实验室特征.
主要方法:
- 分析了777名接受过垂体手术的患者,对183名患者进行了外部验证.
- 开发和比较六个ML模型,使用手术前和后的数据.
- 使用ROC曲线下的面积 (AUC) 和可解释性技术 (PFI,LIME,SHAP) 评估预测准确度.
主要成果:
- 随机森林 (RF) 模型在内部验证中实现了最高的预测准确性 (AUC=0.88).
- 确定了13个关键特征,其中德斯莫普林素剂量,早期术后血清水平和手术持续时间是最重要的.
- 可解释性方法证实了这些已识别的特征的重要性.
结论:
- 基于射频的ML模型使用个性化的患者数据准确预测术后低血症.
- 这种方法有可能通过早期检测和治疗低血症来改善患者的护理.
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