在抗生素管理计划和优化处方实践中采用人工智能驱动的方法:系统审查
Hamid Harandi1, Maryam Shafaati1, Mohammadreza Salehi1
1Research Center for Antibiotic Stewardship and Antimicrobial Resistance, Infectious Diseases Department, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran.
人工智能和机器学习通过改善抗生素选择,耐药性预测和剂量来增强抗菌管理计划 (ASP). 这些人工智能驱动的工具显示出降低死亡率和优化全球抗生素使用的潜力.
科学领域:
- 传染性疾病 传染性疾病
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
背景情况:
- 抗菌素耐药性 (AMR) 是一个关键的全球健康挑战,需要优化抗生素的使用.
- 抗微生物药物管理计划 (ASP) 对于打击AMR至关重要.
- 人工智能 (AI) 和机器学习 (ML) 提供了新的方法来增强ASP.
研究的目的:
- 系统地审查AI驱动工具在ASP中的应用.
- 评估AI在抗微生物药物管理中的方法,结果和挑战.
- 评估AI对抗生素处方准确性,耐药性预测和剂量优化的影响.
主要方法:
- 来自PubMed,Scopus,Web of Science和Embase的研究的系统审查.
- 关键词:"人工智能"和"抗生素".
- 纳入标准:在ASP中使用AI/ML进行实证选择,剂量调整和坚持的研究;使用纽卡斯尔太华尺度评估偏差风险.
主要成果:
- 人工智能/ML模型在优化实证抗生素选择和预测耐药性方面表现出有效性.
- 研究证实了AI在提高治疗适当性的作用,可能降低死亡率.
- 机器学习在优化抗生素剂量方面显示出前景,特别是对于万科米辛.
结论:
- 通过优化干预,选择,预测和剂量,AI和ML显著提高了抗生素管理.
- 人工智能驱动的ASP有可能改善医疗保健结果并打击AMR.
- 高收入国家和低收入/中等收入国家之间在人工智能实施方面存在差异,突出了结构性挑战.
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