预测重症监护室急性损伤的人工智能模型:对建模方法,数据利用和临床适用性的系统审查
Tongyue Shi1,2,3, Yu Lin4, Huiying Zhao5
1National Institute of Health Data Science, Peking University, Beijing 100191, China.
JAMIA open
|July 7, 2025
概括
使用人工智能在重症监护室 (ICU) 预测急性损伤 (AKI) 是有前途的,但模型需要更好的外部验证和临床使用的动态能力.
科学领域:
- 关键护理医学 关键护理医学
- 医疗保健中的人工智能
- 腎臟病學 (nephrology) 是一種醫學專業.
背景情况:
- 急性损伤 (AKI) 是重症监护室 (ICU) 患者的重大并发症,导致高死亡率和医疗费用增加.
- 早期预测AKI对于及时干预和改善患者结果至关重要.
- 现有的预测模型,包括机器学习和深度学习,需要对其方法和临床适用性进行进一步评估.
研究的目的:
- 在ICU中全面审查和评估AKI预测模型的建模方法,数据利用和临床适用性.
- 确定这些模型的开发和实施当前的挑战和局限性.
- 提出未来的研究方向,以提高重症监护机构的AKI预测.
主要方法:
- 在主要的科学数据库 (PubMed,Embase,Scopus,Web of Science,IEEE Xplore,ACM数字图书馆) 进行了系统的文献搜索,截至2024年12月12日.
- 包括报告AKI预测模型的研究,使用至少两个预测因素和绩效评估的ICU数据.
- 在包括的研究中评估了偏差风险 (PROBAST) 和报告质量 (TRIPOD).
主要成果:
- 在1305个选的研究中,有47个符合纳入标准,利用从机器学习到深度学习的各种AI技术.
- 外部验证仅在14项研究中进行,在44项研究中发现了偏差的高风险,特别是在概括性和临床适用性方面.
- 研究设计,预测窗口和数据源的显著异质性使模型比较复杂化.
结论:
- 人工智能模型显示了在ICU中AKI预测的潜力,但面临的挑战包括有限的外部验证,缺乏动态建模和糟糕的解释性.
- 解决这些局限性对于弥合模型开发和临床实践之间的差距至关重要.
- 未来的研究应该专注于开发动态,可解释和外部验证的模型,以提高AI在AKI预测中的现实应用性.
相关概念视频
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