在重症监护病房中使用机器学习模型给药未分离的肝素后对aPTT预测因子的分析
Tadashi Kamio1,2, Masaru Ikegami3, Megumi Mizuno3
1Department of Anesthesiology and Critical Care Medicine, Jichi Medical University Saitama Medical Center, Saitama, Japan.
PloS one
|July 21, 2025
概括
机器学习模型在预测激活的局部血栓形成时间 (aPTT) 方面取得了中等的成功,用于接受未分离肝素治疗的重症监护病房 (ICU) 患者. 虽然在确定阳性病例方面精确,但需要进一步的研究来提高最佳抗凝血的整体预测准确性.
科学领域:
- 医疗信息学 医疗信息学
- 临床药理学 临床药理学
- 人工智能在医学中的应用
背景情况:
- 在重症监护室 (ICU) 中优化肝素抗凝剂是复杂的.
- 精确预测激活的部分血栓形成时间 (aPTT) 对于有效的肝素治疗至关重要.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于预测在接受未分离肝素的ICU患者中aPTT.
- 确定在肝素化过程中影响aPTT水平的关键因素.
主要方法:
- 利用了945名接受未分离肝素治疗的ICU患者 (2018-2022) 的数据.
- 开发了使用静态和动态患者特征来预测aPTT的ML模型.
- 采用随机森林模型和SHAP分析用于绩效评估和因素识别.
主要成果:
- 随机森林模型实现了中等的预测性能 (ROC AUC 0.707-0.732).
- 模型显示高精度但回忆能力较低,表明有效识别真实阳性,但缺少一些病例.
- 包含时间序列数据只能带来边际性能改善.
结论:
- ML模型在治疗肝素的ICU患者中预测aPTT的准确度中等.
- 目前的模型擅长识别阳性病例,但需要改进以获得更广泛的准确性.
- 建议对额外的预测因素进行进一步的研究,以改善个性化的抗凝管理.
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