人工智能和机器学习用于精确的华法林剂量:一个全面的叙事审查.
Mohammadsadra Shamohammadi1, Mohammad Ali Nazari2, Seyedeh Mohadese Mosavi Mirkalaie3
1Gastrointestinal and Liver Diseases Research Center, Iran University of Medical Sciences, Tehran, Iran.
European journal of clinical pharmacology
|February 6, 2026
概括
人工智能和机器学习对优化华法林剂量有希望,可能改善国际规范化比率 (INR) 控制. 需要进一步的研究来证实它们与传统方法相比的临床有效性.
科学领域:
- 药物基因组学 药物基因组学
- 计算生物学 计算生物学
- 临床药理学 临床药理学
背景情况:
- 华法林是一种广泛使用的抗凝剂,具有狭窄的治疗指数,需要密切监测国际规范化比率 (INR).
- 剂量偏差可能导致严重的血栓栓塞或出血事件.
研究的目的:
- 审查和综合有关机器学习 (ML) 方法的文献,以实现华法林剂量个性化.
- 评估在华法林治疗中ML模型的预测性能和临床相关性.
主要方法:
- 使用机器学习技术 (例如,支持向量回归,神经网络,合体模型,强化学习) 进行华法林剂量研究的叙述性综述.
- 专注于结合临床和遗传因素的模型.
主要成果:
- 基于ML的华法林剂量模型显示,与传统方法相比,可以更好地预测治疗剂量和INR调节.
- 现有的ML模型经常受到小样本大小,有限的外部验证和方法异质性的影响,影响了概括性.
结论:
- 人工智能 (AI) 和ML在华法林剂量精度和INR控制方面提供了潜在的优势.
- 关于比较有效性的最终结论需要进一步进行更大的样本大小和严格的外部验证的强有力的研究.
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