在移植中,基于基因算法和深森林的临床导向的塔克罗利斯剂量算法.
Jianliang Min1,2,3,4, Qihao Li1,3,4, Weijie Lai1,3,4
1Organ Transplantation Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Frontiers in pharmacology
|September 15, 2025
概括
人工智能驱动的塔克罗利斯 (TAC) 个性化剂量提高了移植患者的精度. 这种AI模型预测了最佳的TAC剂量,降低了排斥风险并提高了患者的护理.
科学领域:
- 药物基因组学 药物基因组学
- 人工智能在医学中的应用
- 移植医学 移植医学 移植医学
背景情况:
- 塔克罗利 (TAC) 对于预防器官移植排斥至关重要,但具有狭窄的治疗窗口,造成剂量挑战.
- 在TAC新陈代谢的个体变化需要精确的管理,以获得最佳的患者结果.
- 人工智能辅助的个性化剂量提供了一个有希望的解决方案,用于自动和准确的TAC管理.
研究的目的:
- 开发和评估一个以临床为导向的算法,用于预测移植患者的塔克罗利斯 (TAC) 剂量.
- 整合遗传算法 (GA) 和深森林 (DF) 进行精确的初始和后续 TAC 剂量预测.
- 确定关键的临床变量,以准确和用户友好的TAC剂量.
主要方法:
- 采用了一种混合方法,将遗传算法 (GA) 和深森林 (DF) 结合起来.
- 使用支持向量回归 (RBF内核) 的 GA 从临床因素中优化了候选变量.
- 详尽的特征选择确定了模型输入的关键临床变量.
主要成果:
- 开发的DF模型在初始TAC剂量预测中实现了84.5%的准确性.
- 该模型显示,在288名接受者的队列中,随访TAC剂量预测的准确率为91.7%.
- 关键临床变量的整合提高了预测性能.
结论:
- 拟议的人工智能驱动的算法提供了一个可靠的方法,用于个性化的tacrolimus剂量.
- 这种方法为临床实践中的基于算法的自动药物剂量管道提供了潜在的参考.
- 这项研究强调了人工智能的潜力,以改善移植患者免疫抑制剂的管理.
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