使用机器学习优化对心房的患者的华法林剂量
Jeremy Petch1,2,3,4, Walter Nelson5,6, Mary Wu7
1Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada. petchj@hhsc.ca.
Scientific reports
|February 24, 2024
概括
深度强化学习优化了对心房患者的华法林剂量,改善了治疗INR范围中的时间,并减少了不良事件. 这种人工智能方法在全球范围内加强了中风预防策略.
科学领域:
- 人工智能在医学中的应用
- 药理学和药物剂量优化 药理学和药物剂量优化
- 临床决策支持系统 临床决策支持系统
背景情况:
- 华法林是一种维生素K对抗剂,广泛用于心房中风预防,但由于复杂的药理动力学,它带来了挑战,往往导致低于最佳的抗凝.
- 达到并保持国际规范化比率 (INR) 目标范围 (2.0-3.0) 对于华法林的有效性和安全性至关重要,但在临床实践中难以持续实现.
研究的目的:
- 开发和验证一个深度强化学习 (DRL) 模型,以优化华法林剂量,最大限度地提高治疗INR范围 (TTR) 的时间.
- 评估DRL模型在改善临床结果方面的有效性,特别是减少中风,全身栓塞或大出血的复合终点.
主要方法:
- 为了创建DRL模型,使用了批量约束深度Q学习算法的新型半马尔科夫决策过程制定.
- 该模型是通过3项主要临床试验 (ENGAGE AF-TIMI 48,ARISTOTLE,ROCKET AF) 的22502名华法林治疗患者的数据进行训练的.
- 外部验证是使用RE-LY试验中5730名华法林治疗患者的数据进行的,将算法一致的剂量与TTR和临床结果进行比较.
主要成果:
- 外部验证表明,中心级算法一致的剂量和TTR (R2 = 0.56) 之间存在显著的正相关性.
- 算法一致剂量的10%增加与TTR的6.78%改善和复合临床结果的11%降低相关.
- DRL算法的性能与基于规则的临床算法相当,表明其稳定性和临床应用潜力.
结论:
- 深度强化学习算法可以有效地优化华法林剂量,以改善心房的患者治疗INR范围中的时间.
- 基于该DRL算法实施数字临床决策支持系统有望加强华法林管理并改善全球患者的治疗结果.
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