基于模型的精确剂量:治疗药物监测的概念框架 整合机器学习和人工智能 在人口健康信息学中
Jennifer Le1,2, Hien N Le1, Giang Nguyen1
1Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA.
Journal of personalized medicine
|February 26, 2026
概括
与AI和EHR集成的模型信息精确剂量 (MIPD) 提高了药物安全性和有效性. 这种方法可以实时调整剂量,改善结果,特别是对于脆弱的患者.
科学领域:
- 药理学 药理学是指药理学的学科.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 传统的治疗药物监测有其局限性,包括手动解释和稳定状态采样要求.
- 基于模型的精确剂量 (MIPD) 在人口健康信息学中为药物安全性和疗效提供了一个有前途的解决方案.
- 本研究探讨了MIPD与AI,ML和EHR的整合,以提高精确剂量.
研究的目的:
- 探索MIPD在人口健康信息学中的整合.
- 评估AI/ML和EHR在提高剂量精度方面的潜力.
- 确定对弱势群体的影响.
主要方法:
- 在PubMed和Embase搜索同行评审的研究 (1958-2024年).
- 包括MIPD,人口健康和AI/ML对个性化剂量的研究.
- 专注于重症患者,老年患者和儿科患者.
主要成果:
- 使用贝叶斯方法的MIPD是一种可扩展的精密医学创新.
- AI/ML与EHR相结合,可以实时进行精确的剂量调整.
- 潜在的好处包括改善患者安全,优化结果和降低成本,特别是在弱势群体.
结论:
- 需要进一步的研究来定义,实施和评估MIPD中的实际AI/ML应用.
- 制定标准和确定合适的药物对于推进个性化医学的发展至关重要.
- 专业人员之间的协作和安全的数据管理对于成功实施至关重要.
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