机器学习算法预测长期术后阿片类药物滥用:系统性审查
Omar S Emam1, Abdullah S Eldaly2, Francisco R Avila1
1Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL, USA.
The American surgeon
|September 21, 2023
概括
机器学习模型在预测长期的术后阿片类药物使用方面表现有前途. 这些算法,特别是基于决策树的算法,可以帮助临床医生识别有风险的患者.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 公共卫生 公共卫生
背景情况:
- 美国面临严重的阿片类药物危机,需要工具来预测长期的术后阿片类药物 (PPO) 使用.
- 机器学习 (ML) 为识别患有PPO风险的患者提供了潜在的解决方案.
结论:
- 机器学习算法显示出作为预测PPO的决策支持工具的潜力.
- 临床整合需要进一步验证.
- 这些工具可以帮助减轻阿片类药物流行病的影响.
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