机器学习对了解急性损伤的方法:当前趋势和未来方向
Inyong Jeong1, Nam-Jun Cho2, Se-Jin Ahn1
1Department of Medical Informatics, College of Medicine, Korea University, Seoul, Korea.
The Korean journal of internal medicine
|October 29, 2024
概括
人工智能 (AI) 在急性损伤 (AKI) 的早期检测和预后方面表现有前途. 然而,为了有效实施,必须解决数据预处理,模型限制和临床集成方面的挑战.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 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) 是一个重大的全球健康挑战,导致患者的不良结果和相当大的经济成本.
- 现有的研究重点是预防和预测AKI,最近关注的是利用人工智能 (AI) 为这些目的.
研究的目的:
- 综合审查AI用于AKI预测的应用中的最新进展.
- 识别和讨论阻碍基于AI的AKI预测系统临床整合的挑战.
主要方法:
- 关于AI用于AKI预测的最新研究的文献综述.
- 分析与数据预处理,患者识别和模型限制相关的挑战.
- 讨论成功临床部署人工智能技术的要求.
主要成果:
- 人工智能证明了早期AKI检测和预后的潜力.
- 临床实践整合存在重大挑战,包括追溯数据识别和信息预处理.
- 当前人工智能模型的局限性和部署约束阻碍了现实世界的应用.
结论:
- 标准化标签标准和国际多机构合作对于高质量的数据收集至关重要.
- 提高AI技术的可靠性和解决部署约束对于临床适用性至关重要.
- 克服这些挑战将改善AKI临床支持系统,从而改善患者的预后.
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