对抗药物的药物相互作用警报的不一致性:电子数据库的比较分析和人工智能工具的解释性见解
Thitipon Yaowaluk1, Supawit Tangpanithandee2, Pinnakarn Techapichetvanich2
1Drug Information Service and Siriraj Poison Control Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
Drug design, development and therapy
|September 2, 2025
概括
抗药与常见药物之间的药物相互作用 (DDI) 显示了数据库中的重大不一致性. 人工智能 (AI) 通常支持更严格的评级,强调需要协调标准和临床医生的意识,以确保安全的患者护理.
科学领域:
- 药理学
- 临床药房
- 医疗信息学
背景情况:
- 药物相互作用 (DDI) 是一个临床关键问题,特别是当多种药物,包括抗药物被使用时.
- 虽然抗药物是必不可少的,但它们与其他药物的有害相互作用的可能性还没有得到充分的研究.
研究的目的:
- 评估常用药物和抗毒剂之间的潜在DDI.
- 评估两个电子数据库与人工智能 (AI) 平台之间的一致性.
主要方法:
- 对50种常用药物和主要抗药物进行了描述性分析.
- 使用Micromedex和WebMD数据库以及ChatGPT和谷歌双子AI进行了潜在的交互评估.
- 分析了DDI的严重程度,文档质量和数据库/AI协议 (kappa统计).
主要成果:
- 确定了154个潜在的DDI对,数据库之间的一致性很差 (kappa = -0.126).
- 发现了19种严重的DDI,主要涉及血清激素综合征和QT延长,其中甲基蓝和精神药物是主要的贡献者.
- 在不一致的情况下,人工智能模型倾向于与更严格的DDI评级保持一致,提供一种保守的方法.
结论:
- 数据库和人工智能之间的DDI识别存在显著的不一致性,需要协调评估标准.
- 临床医生必须意识到数据库之间的变化,以确保安全有效的患者护理.
- 综合性DDI查和共享决策对于管理潜在药物相互作用至关重要.
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