智能紧急护理:对预测机器学习模型的叙述性审查
David B Olawade1,2,3,4, Adebayo Da'Costa5, Joseph E Origbo6
1Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London, UK.
Annals of translational medicine
|November 10, 2025
概括
机器学习 (ML) 模型对预测急诊室 (ED) 患者的结果,如死亡率和ICU入院,显示出希望. 虽然有效,但数据质量和通用性等挑战需要解决,以实现更广泛的临床采用.
科学领域:
- 紧急医疗 紧急医疗
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 紧急服务部门 (ED) 需要及时评估患者的结果,以获得最佳的护理和资源管理.
- 机器学习 (ML) 为预测关键ED结果提供了先进的工具.
研究的目的:
- 对ED结果 (死亡率,ICU入院,出院) 进行基于ML的预测模型的审查.
- 确定ED中ML的局限性和未来研究方向.
主要方法:
- 对ED结果的ML模型的叙述性审查 (2015年1月至2024年12月).
- 分析机器学习技术,数据源和评估指标.
- 查了156项研究,其中包括45项.
主要成果:
- 机器学习模型实现了ED结果的高预测精度 (AUC-ROC 0.75-0.95).
- 合并方法和神经网络表现强.
- 个性化和可解释的AI (XAI) 提高了准确性和可解释性.
- 局限性包括数据异质性和不良概括性.
结论:
- ML正在改变ED预测建模和临床决策.
- 个性化和可解释的模型增强了信任和可用性.
- 需要进一步研究数据质量,标准化指标和多中心验证.
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