使用机器学习方法预测成年人全因体内住院病例:系统性审查.
Mohsen Askar1, Masoud Tafavvoghi2, Lars Småbrekke1
1Faculty of Health Sciences, Department of Pharmacy, UiT-The Arctic University of Norway, Tromsø, Norway.
PloS one
|August 23, 2024
概括
机器学习 (ML) 显示出预测成人入院和再入院的前景. 然而,重要的方法和报告质量问题阻碍了临床采用,需要改进研究标准.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健服务研究 医疗服务研究
- 计算医学是一种计算医学.
背景情况:
- 机器学习 (ML) 越来越多地被用于预测患者住院治疗.
- 准确预测住院和再住院的情况对于医疗保健资源管理和患者的治疗结果至关重要.
研究的目的:
- 系统地审查和分析机器学习 (ML) 在预测成年人所有原因的体质住院和再住院的利用.
- 识别基于ML的住院预测模型中使用的常见方法,特性和性能指标.
主要方法:
- 在八个数据库中进行了全面的文献搜索,截至2023年10月.
- 包括使用ML预测成人入院/再入院的研究,使用CHARMS提取数据,并通过PROBAST和TRIPOD进行评估.
主要成果:
- 116项研究符合纳入标准;45项预测入院,70项预测再入院. 提升基于树的算法和临床笔记的自然语言处理 (NLP) 显示出强的表现.
- 数据集,算法和验证方法存在显著的变化. 报告质量和方法严谨性通常很差,临床实施有限.
- 关键的不足包括缺乏个体患者层面的解释,缺少代码可用性,外部验证不足和校准差.
结论:
- 对用于医院住院预测的ML研究面临着实质性的方法和报告质量挑战.
- 提高研究质量和透明度对于这些预测模型的临床接受和实施至关重要.
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