对外部验证的机器学习模型进行系统审查,以预测一般医院患者的急性损伤
Marina Wainstein1,2, Emily Flanagan3, David W Johnson4,5,6
1Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia.
Frontiers in nephrology
|September 7, 2023
概括
机器学习模型显示出预测住院患者急性损伤 (AKI) 的前景. 然而,目前的模型在不同的人群中面临着局限性,需要更简单,更适应的预测工具来广泛临床使用.
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) 是住院患者经常发生的严重并发症.
- 早期预测AKI风险可以使干预措施能够减轻患者的伤害.
- 机器学习 (ML) 模型为处理复杂的健康数据提供了潜在的可能性,以准确预测AKI.
研究的目的:
- 识别和评估外部验证的ML模型,用于在一般医院人群中预测AKI风险.
- 评估现有的ML模型在AKI预测中的适用性和局限性.
主要方法:
- 进行了系统的文献搜索,以找到AKI的外部验证的ML模型.
- 研究的选基于一般医院人群的发展和使用当前的AKI定义.
- 三项研究符合从889篇文章的初步选中获得的纳入标准.
主要成果:
- 三种已识别的ML模型表现出良好的性能和健全的方法.
- 关键问题包括有限的人口多样性,可比的数字环境,广泛的预测变量和过度依赖特定的生物标志物.
- 这些局限性可能会限制模型在各种社会经济和文化环境中的通用性.
结论:
- 目前用于AKI预测的ML模型对于广泛的临床应用具有关键限制.
- 需要更简单,更易于传输的预测模型.
- 未来的模型应该旨在实现更广泛的适用性和与当前诊断工具相比的竞争优势.
相关概念视频
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