在医院环境中使用机器学习或深度学习模型来检测不适当的处方:系统性审查
Erin Johns1,2, Ahmad Alkanj3, Morgane Beck4
1Direction de la Qualité, de la Performance et de l'Innovation, Agence Régionale de Santé Grand Est Site de Strasbourg, Strasbourg, Grand Est, France erin.johns@etu.unistra.fr.
人工智能 (AI) 在检测不合适的医院药物订单方面表现有前途. 虽然目前的研究是初步的,但人工智能工具为临床医院药房实践提供了潜在的价值.
科学领域:
- 药房 药房 药房 药房
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 人工智能 (AI) 越来越与医院药房相关,这是由于大量的健康数据可用性.
- 人工智能模型有可能对当前的药房实践和药物管理产生重大影响.
研究的目的:
- 系统地审查机器学习和深度学习模型的当前状态,以检测不适当的医院药物订单.
主要方法:
- 根据PRISMA指南进行了系统审查.
- 在MEDLINE和Embase数据库中搜索到2023年5月为医院药剂师的AI模型研究.
- 使用预测模型的偏差风险评估工具 (PROBAST) 评估偏差风险.
主要成果:
- 选择了13篇文章,12篇文章具有偏见的高风险.
- 大多数研究 (11) 在2020-2023年期间发表,主要是在北美和亚洲.
- 人工智能模型,主要是监督学习,分析药物订单以检测不适当的处方,包括抗生素耐药性和剂量错误.
结论:
- 目前很少有原始研究报告了医院临床药房的AI工具.
- 现有的研究,虽然初步,表明AI在临床医院药房的潜在价值.
- 需要进一步的研究来验证和有效地实施AI工具在药物订单审查中.
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