机器学习在华法林剂量精度的应用在接受他类药物治疗的糖尿病患者中:一项比较研究
Mobina Ghajar1, Mandana Sadat Ghafourian2, Sara Tarkiani1
1Department of Cardiology, Zanjan University of Medical Science, Zanjan, Iran.
Cardiovascular drugs and therapy
|May 31, 2025
概括
接受他类药物治疗的糖尿病患者需要根据使用的特定他类药物不同剂量的华法林. 机器学习模型可以帮助预测最佳的华法林剂量,以减少不良影响.
科学领域:
- 药物基因组学和个性化医学.
- 人工智能在临床实践中的应用.
背景情况:
- 华法林是一种广泛使用的抗凝剂,治疗指数狭窄.
- 在糖尿病患者中,静止剂治疗是常见的,可能会影响华法林代谢.
- 准确的华法林剂量对于预防出血和血栓塞栓事件至关重要.
研究的目的:
- 评估他类药物治疗如何影响糖尿病患者的华法林剂量要求.
- 评估各种机器学习算法的预测性能,以获得最佳的华法林剂量.
主要方法:
- 对包括628名患者在内的数据集的分析 (216名糖尿病患者,412名非糖尿病患者).
- 包括患者特征:身高,体重,性别,种族,年龄,INR,目标INR,华法林剂量,他类药物的使用和适应症.
- 应用六种回归模型:LASSO,KNN,SVR,LR,决策树和随机森林用于剂量预测.
主要成果:
- 糖尿病患者需要平均每周华法林剂量为38.73毫克 (SD 15.37),而非糖尿病患者需要34.50毫克 (SD 18.27).
- 特定的他类药物,如阿托瓦斯塔丁和罗斯瓦斯塔丁,与洛瓦斯塔丁和弗鲁瓦斯塔丁相比,在糖尿病患者中与不同的华法林剂量需求有关.
结论:
- 华法林剂量要求在接受他类药物治疗的糖尿病患者中有所不同,具体取决于具体的他类药物.
- 机器学习模型在预测华法林剂量方面表现有前途,有可能减轻药物不良事件.
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