用于急性损伤预测和管理的机器学习模型:对外部验证的研究进行范围审查
Aqeeb Ur Rehman1, Javier A Neyra1, Jin Chen1
1Division of Nephrology, Department of Internal Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.
Critical reviews in clinical laboratory sciences
|May 5, 2025
概括
机器学习 (ML) 模型在急性损伤 (AKI) 预测和管理方面显示出前景. 然而,可变的性能和缺乏现实世界的验证凸显了概括性和临床实施方面的挑战.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 人工智能的人工智能
- 临床信息学 临床信息学
背景情况:
- 急性损伤 (AKI) 显著影响患者的治疗结果,死亡率和医疗保健费用.
- 机器学习 (ML) 提供了早期AKI预测,检测和个性化管理的潜力.
- 目前用于AKI的ML模型在外部验证和现实世界的临床应用方面面临挑战.
研究的目的:
- 为AKI预测,检测和管理进行外部验证的ML模型的全面范围审查.
- 分析在AKI护理中的ML模型的性能,概括性和临床实用性.
- 确定空白和未来发展方向,为AKI开发可靠和临床适用的ML解决方案.
主要方法:
- 在MEDLINE,EMBASE,Web of Science和Scopus上进行系统的文献搜索,截至2024年2月15日.
- 包括使用KDIGO AKI定义进行外部验证的ML模型 (不包括后勤回归) 的研究.
- 分析了44项研究,包括成人和儿科患者的161个ML模型,涉及400多万患者入院.
主要成果:
- 由于不同的临床环境,种群和预测因素,ML模型在外部验证中显示出可变的性能.
- 大多数模型都是为专门人群开发的 (例如,ICU,术后),很少包含动态预测因素.
- 没有任何经过审查的模型被验证或在现实世界的临床工作流中实施,这表明可重现性和通用性存在挑战.
结论:
- 标准化绩效指标和验证协议对于提高AKIML模型的临床适用性至关重要.
- 未来的研究应该专注于动态预测因素,非结构化数据,多样化的人口和现实世界的实施.
- 临床医生和数据科学家之间的合作对于开发临床相关,公平和实用的ML工具来管理AKI至关重要.
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