对于患有急性损伤的成年患者表型的方法:系统性审查
Anjay P Shah1,2, William Snead1,2, Anshul Daga1,2
1Intelligent Clinical Care Center, University of Florida, Gainesville, FL, USA.
Journal of nephrology
|September 16, 2024
概括
使用机器学习辅助工具识别急性损伤 (AKI) 亚现象型个性化治疗. 然而,目前的方法显示出可变性,需要在不同患者群体中验证以改善临床应用.
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
- 生物统计学 生物统计学
背景情况:
- 急性损伤 (AKI) 呈现出不同的临床特征和机制.
- 人工智能 (AI) 便于识别AKI亚型,以进行量身定制的治疗和预后.
研究的目的:
- 系统地审查机器学习应用程序,以识别成年AKI亚型.
- 分析已识别的AKI亚型的方法,歧视因素和临床关联.
主要方法:
- 系统性文献综述 (2017-2022) 使用机器学习进行AKI亚基类型化研究的研究.
- 提取患者人口统计数据,集群方法,歧视因素和验证.
主要成果:
- 在不同人群中观察到AKI亚表型识别的显著变化.
- 临床数据 (并发症,实验室值) 和生物标志物用于患者分组.
- AKI亚表型和结果 (死亡率,恢复,治疗反应) 之间的关联是异质的.
- 不同的聚类技术导致了变化,限制了跨人群的适用性.
结论:
- 识别AKI亚现象型可以提高对个体患者病历的理解和管理.
- 未来的研究需要在更大,更多样化的队列中进行验证,以提高临床效用和个性化的AKI医学.
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