华法林治疗的机器学习:系统性审查
Pavol Fülöp1, Štefan Tóth2, Tibor Porubän3
12nd Department of Cardiology, East Slovak Institute of Cardiovascular Diseases, Faculty of Medicine, Pavol Jozef Šafárik University in Košice, Ondavská 8, 040 11 Košice, Slovakia.
Pharmaceuticals (Basel, Switzerland)
|October 29, 2025
概括
机器学习 (ML) 在预测华法林剂量方面表现有前途,比传统方法提高了准确性. 然而,缺乏安全数据阻碍了临床使用,需要进行前性试验以确保安全实施.
科学领域:
- 药理学 药理学是指药理学的学科.
- 人工智能的人工智能
- 临床信息学 临床信息学
背景情况:
- 华法林仍然对特定的患者群体至关重要,尽管新的抗凝药.
- 传统的华法林剂量具有低于最佳的治疗范围 (55-65%).
- 低于最佳剂量会增加出血和血栓事件的风险.
研究的目的:
- 系统地审查用于华法林剂量预测的机器学习 (ML) 方法.
- 评估ML算法在华法林剂量中的性能和局限性.
主要方法:
- 从2022-2025年对14项研究 (122,400名患者) 的系统审查.
- 包括使用ML用于华法林剂量与性能指标的研究.
- 使用PROBAST进行偏差风险评估.
主要成果:
- 强化学习在实现治疗范围方面表现优越.
- 机器学习模型在准确度指标 (例如,平均绝对误差) 中始终优于传统方法.
- 显著的局限性包括回顾性研究的主导地位和缺乏安全结果数据.
结论:
- ML算法显示了提高华法林剂量准确性的潜力.
- 缺乏可靠的安全结果数据 (出血,血栓塞栓症) 是临床实施的主要障碍.
- 采用安全终点和多种人群验证的多中心前性试验对于采用至关重要.
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