在移植护理中使用人工智能驱动的塔克罗利剂量:队列研究
Mingjia Huo1, Sean Perez2, Linda Awdishu3
1Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, United States.
JMIR AI
|September 2, 2025
概括
这项研究开发了一种人工智能模型,用于预测移植患者的下一天的塔克罗利木斯水平,旨在优化免疫抑制并防止过低或过量服用. 这种模型有望在临床实践中指导精确的塔克罗利木斯剂量.
科学领域:
- 移植医学
- 药物动力学
- 医疗保健中的人工智能
背景情况:
- 在固体器官移植中,塔克罗利木斯对免疫抑制至关重要,但其治疗范围有限.
- 由于患者,捐赠者和与药物相关的因素,移植后确切的塔克罗利木斯剂量具有挑战性.
- 保持最佳药物水平对于防止移植体排斥和毒性至关重要.
研究的目的:
- 设计和验证用于预测第二天塔克罗利斯最低度 (C0) 的机器学习模型.
- 为指导塔克罗利木斯的剂量策略,并防止移植患者持续低剂量或过量剂量.
- 利用人工智能进行个性化免疫抑制治疗.
主要方法:
- 使用1597名脏和肝脏移植患者的回顾性数据开发了一种长期短期记忆模型 (LSTM).
- 包括移植类型,人口统计,并发症,生命体征,实验室,饮食和药物等预测因素.
- 使用平均绝对误差和对剂量准确性的3类分类来评估模型性能,并生成剂量建议.
主要成果:
- 在预测第二天的tacrolimus C0时,LSTM模型的平均绝对误差为1. 880 ng/ ml.
- 主要预测因素包括最近的塔克罗利木斯C0,剂量,移植类型,饮食和药物相互作用.
- 该模型显示了0. 653的微平均F1分数,用于分类低剂量,治疗剂量和过量剂量.
结论:
- 这项研究提供了最大的TA应用之一,用于塔克罗利木斯剂量,具有有效的LSTM模型用于C0预测.
- 该模型显示了在移植护理中准确的塔克罗利木斯剂量建议的潜力.
- 需要进行前性研究来确认该模型在剂量调整中的实际有效性.
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