在后急性护理环境中基于机器学习的感染诊断和预后模型:系统审查
Zidu Xu1, Danielle Scharp1, Mollie Hobensack2
1School of Nursing, Columbia University, New York, NY 10032, United States.
Journal of the American Medical Informatics Association : JAMIA
|November 12, 2024
概括
机器学习模型对预测急性后护理 (PAC) 中的感染有希望,但对先进方法的证据是有希望的.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 预测建模预测建模
背景情况:
- 后急性护理 (PAC) 设置在早期感染诊断和预后方面面临挑战.
- 机器学习 (ML) 提供了改善PAC感染相关结果的潜力.
- 现有的ML模型需要对质量,限制和公平性进行评估.
研究的目的:
- 在PAC中审查ML模型用于感染诊断和预后.
- 确定感染相关结果的关键风险预测因素.
- 评估PAC当前ML模型的质量和局限性.
主要方法:
- 在主要数据库 (PubMed,科学网等) 进行系统的文献搜索. 在2024年2月.
- 包括开发和评估ML模型的研究,使用PAC数据来评估感染风险.
- 通过CHARMS检查清单提取数据,并使用PROBAST工具进行质量评估.
主要成果:
- 包括13项研究,主要是关于养老院呼吸道感染的研究.
- 自2020年以来,向先进的ML算法和多模式数据 (包括非结构化数据) 的趋势.
- 对高级ML优势的证据不足;常见的偏见包括缺乏外部验证和校准差.
结论:
- 在PAC感染预测中,先进的ML模型显示出有限的优势证据.
- 未来的研究需要一个社会生态的方法,用于预测因素的选择和模型的开发.
- 严格的方法和公平性考虑对于在PAC中开发ML模型至关重要.
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