通过机器学习预测内射分析性低血压:一个系统性审查
Jacob Ninan1, Nasrin Nikravangolsefid2, Hong Hieu Truong2,3
1Department of Nephrology and Critical Care Medicine, MultiCare Capital Medical Center, Olympia, WA, USA. jacob.ninan@multicare.org.
Journal of nephrology
|May 3, 2025
概括
机器学习 (ML) 模型对预测内透析性低血压,即血液透析的严重并发症具有前景. 然而,大多数研究都有很高的偏差风险,限制了它们的临床适用性.
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
- 生物医学信息学 生物医学信息学
背景情况:
- 透析内低血压 (IDH) 是血液透析期间的一种严重并发症,与患者发病率和死亡率的增加有关.
- 机器学习 (ML) 算法越来越多地被探索用于预测IDH,旨在改善患者的治疗结果.
- 系统性审查对于评估IDH预测ML模型的当前状态至关重要.
研究的目的:
- 系统审查机器学习 (ML) 模型,这些模型是为预测日内分析性低血压 (IDH) 开发的.
- 评估这些ML模型的性能,方法质量和临床适用性.
- 为了确定基于ML的IDH预测的差距和未来方向.
主要方法:
- 根据一个注册的协议 (PROSPERO ID:CRD42022362194) 进行了系统审查.
- 从创建到2023年7月20日,搜索了六个数据库.
- 两个独立的审查者评估了16项研究 (14,500名患者),并使用PROBAST评估了偏见.
主要成果:
- 包括16项涉及14500名血液透析患者的研究.
- 发现存在高偏差风险 (14/16项研究),质疑研究结果的可靠性.
- 神经网络模型是最常见的 (13项研究),具有不同的预测性能 (AUROC: 0.684-0.978).
结论:
- 虽然ML模型显示了预测静脉透析性低血压的潜力,但仍然存在重大挑战.
- 缺乏外部验证和模型异质性阻碍了广泛的临床采用.
- 未来的研究必须优先考虑强大的外部和临床验证,以便实际实施.
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