预测医院病房从紧急部门的入院:一个系统审查
Nekane Larburu1,2, Laiene Azkue1,3, Jon Kerexeta1,2
1Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), 20009 Donostia, Spain.
Journal of personalized medicine
|May 27, 2023
概括
机器学习模型可以识别急诊室 (ED) 的高风险患者,以获得更好的护理. 使用年龄和分类类别的后勤回归,显示了病房入院的强大预测能力.
科学领域:
- 紧急医疗 紧急医疗
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 紧急诊所 (ED) 面临着严重的患者数量和医生限制.
- 改善ED管理需要识别高风险患者,以便及时介入.
- 机器学习 (ML) 提供了在医疗保健环境中开发预测模型的潜力.
研究的目的:
- 系统地审查用于检测ED病房入院的预测模型.
- 为了确定最佳的预测算法,评估他们的预测能力,并评估研究质量.
- 为了确定这些模型中使用的关键预测变量.
主要方法:
- 遵循PRISMA方法的系统审查.
- 在PubMed,Scopus和谷歌学者数据库中进行文献搜索.
- 使用QUIPS工具进行的包含研究的质量评估.
主要成果:
- 在367篇文章的初步搜索中,发现了14篇相关文章.
- 后勤回归成为最常用的预测模型.
- 预测模型实现了曲线下的面积 (AUC) 值,范围从0.75到0.92.
- 患者年龄和ED分类类别是最常用的预测变量.
结论:
- 人工智能 (AI) 模型可以提高EDs中的护理质量.
- 实施人工智能可以帮助减轻医疗保健系统的压力.
- 在ED环境中的预测建模显示出改善患者流动和资源配置的希望.
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