在接受维持血液透析的患者中,用计算机辅助处方激素生成刺激剂:用于人工智能模型选择的随机对照试验
Lee-Moay Lim1,2, Ming-Yen Lin1, Chan Hsu3
1Division of Nephrology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan.
JAMIA open
|March 31, 2025
概括
一个人工智能 (AI) 模型,REEM树,通过优化红色素形成刺激剂 (ESA) 剂量,证明在治疗血液透析患者的贫血方面与医生一样有效. 这种人工智能方法有助于安全地保持目标血红蛋白水平.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 人工智能在医学中的应用
- 临床试验研究 临床试验研究
背景情况:
- 血液透析 (HD) 患者的贫血管理通常涉及红色素形成刺激剂 (ESAs).
- 使用机器学习 (ML) 预测最佳的ESA剂量仍然是一个挑战.
- 评估当代人工智能 (AI) 模型对ESA剂量处方至关重要.
研究的目的:
- 评估人工智能模型在给HD患者处方ESA剂量的有效性.
- 将人工智能驱动的ESA剂量与医生指导的建议进行比较.
- 确定AI能否维持HD贫血管理的血红蛋白目标水平.
主要方法:
- 一个双盲,随机对照试验,将传统医生 (Dr) 组与人工智能组进行比较.
- 人工智能组使用随机效应 (REEM) 树,混合效应随机森林 (MERF) 和长短期记忆 (LSTM) 网络进行ESA剂量预测.
- 主要结果:保持血红蛋白 (Hb) 接近11g/dL (±0.25g/dL);次要结果:Hb在10-12g/dL之间.
主要成果:
- 104名参与者完成了这项研究;平均Hb水平为10.8g/dL (AI) 和10.9g/dL (Dr).
- 在AI组的69.7%和Dr组的73.5%实现了目标Hb水平 (10-12g/dL).
- 只有REEM树人工智能模型满足了初级和二级结果的非劣势标准;在组之间没有显著的不良事件有所不同.
结论:
- 在REEM树AI模型中,在HD患者的ESA剂量管理中,REEM树AI模型证明与医生主导的管理没有劣势.
- 人工智能有效地帮助维持治疗性血红蛋白水平在目标范围内.
- 这项研究支持AI在优化血液透析患者贫血治疗方面的潜力.
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