人工智能改善抗生素处方:一个系统审查
Doaa Amin1, Nathaly Garzόn-Orjuela1, Agustin Garcia Pereira2
1School of Public Health, Physiotherapy & Sports Science, University College Dublin, Belfield, Dublin 4, Dublin, Ireland.
Antibiotics (Basel, Switzerland)
|August 26, 2023
概括
人工智能 (AI) 和机器学习 (ML) 在改善抗生素处方和减少抗生素耐药性 (ABR) 方面表现有前途. 然而,对于这些人工智能工具的临床实施和处方者反,需要进一步研究.
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
- 传染性疾病 传染性疾病
背景情况:
- 抗生素耐药性 (ABR) 是一个日益增长的全球健康威胁.
- 过度使用抗生素 (AB) 显著导致ABR.
- 人工智能 (AI) 为优化AB处方提供了潜在的解决方案.
研究的目的:
- 审查人工智能在改善人类患者抗生素处方方面的应用.
- 评估人工智能对减少抗生素处方和预测不适当使用的影响.
主要方法:
- 使用人工智能进行抗生素处方的观察性研究的系统审查.
- 收录标准:没有时间,场景或语言的限制.
- 使用NIH质量评估工具进行偏差风险评估.
主要成果:
- 包括使用监督机器学习 (ML) 模型的五项研究.
- ML模型证明了改善抗生素处方的积极贡献.
- 研究表明,减少了抗生素处方和预测不适当的处方.
结论:
- ML方法显示了改善各种医疗保健环境中的抗生素处方的潜力.
- 对人工智能模型的临床实施和处方者反需要进一步评估.
- 将AI整合到临床实践中需要仔细考虑可用性和可靠性.
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