生物标志物与机器:预测急性损伤的竞赛
Lama Ghazi1, Kassem Farhat2, Melanie P Hoenig3
1Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL 35294, United States.
Clinical chemistry
|February 1, 2024
概括
早期发现急性损伤 (AKI) 是至关重要的. 新兴的生物标志物和人工智能 (AI) 显示了比传统方法更早诊断AKI的前景,改善了患者的治疗结果.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 生物标志物发现发现
- 人工智能在医学中的应用
背景情况:
- 急性损伤 (AKI) 影响到15%的住院患者,往往通过血清肌素晚诊断.
- AKI在临床上是无声的,需要改进早期检测以预防严重的发病率和死亡率.
研究的目的:
- 审查和批判性地评估最近在AKI检测和预测方面的进展.
- 讨论新兴的生物标志物和人工智能工具,用于成人和儿科患者的AKI诊断.
主要方法:
- 对新生物标志物的临床实用性研究的审查 (例如,cystatin C,NGAL,TIMP-1,IGFBP7).
- 评估用于AKI检测和预测的机器学习算法.
- 对临床实践采用的指导方针和建议进行分析.
主要成果:
- 新兴的生物标志物如囊素C,NGAL和TIMP-1/IGFBP7的组合显示出早期AKI检测的潜力.
- 机器学习算法在预测即将发生的AKI方面表现出高准确性.
- 对某些生物标志物的监管批准意味着临床采用取得了进展.
结论:
- 新的生物标志物和人工智能工具为改善AKI检测和预测提供了重大前景.
- 进一步的临床结果研究对于验证这些新兴技术的实际实用性至关重要.
- 将这些工具纳入临床实践需要仔细考虑和进一步研究.
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