对预测急性损伤的人工智能算法的系统审查
M R Bacci1, C V B Minczuk, F L A Fonseca
1Nephrology Department, Centro Universitário Faculdade de Medicina do ABC, Santo Andre, Brazil. marcelo.bacci@fmabc.net.
European review for medical and pharmacological sciences
|November 2, 2023
概括
机器学习 (ML) 能够有效地预测住院成人的急性损伤 (AKI),在各种病因方面显示出有希望的结果. 未来的研究应该专注于多样化的群体和前性试验,以提高概括性.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 急性损伤 (AKI) 是住院患者死亡和医疗保健成本增加的重要原因.
- 早期识别AKI对于及时干预和改善患者结果至关重要.
- 机器学习 (ML) 为分析复杂的健康数据和预测临床事件提供了先进的功能.
结论:
- ML模型通常满足预先定义的AKI预测在住院成人的性能值 (AUC≥0.70).
- 限制包括潜在的过拟合,缺乏模型解释性 ("黑子"问题),以及许多研究中缺乏人群多样性,阻碍了外部通用性.
- 进一步的研究应优先考虑与多样化的患者群体进行前性,受控试验,以验证和完善基于ML的AKI预测策略.
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