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使用人工智能和机器学习技术的糖尿病足分类模型:系统审查
Manuel Alberto Silva1,2, Emma J Hamilton3,4, David A Russell5,6
1USF Sanus Carandá, ULS Braga, Braga, Portugal.
Journal of medical Internet research
|September 24, 2025
概括
机器学习 (ML) 模型对预测糖尿病足 (DFU) 的结果显示出希望. 然而,高偏差和缺乏外部验证限制了当前的临床使用,需要进一步研究.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 糖尿病学 糖尿病学
背景情况:
- 糖尿病足 (DFU) 是一种重要的糖尿病并发症,影响生存和生活质量.
- 目前的DFU分类系统缺乏个别预测能力.
- 机器学习 (ML) 模型被提出作为改善DFU特征和预测的解决方案.
研究的目的:
- 系统地识别和评估ML模型的证据,预测DFU患者的临床结果.
- 评估现有的基于ML的DFU预测模型的预测准确度和局限性.
主要方法:
- 在主要数据库 (MEDLINE,Scopus,Web of Science,IEEE Xplore) 进行全面的文献搜索,截至2023年7月.
- 包括至少80%的成年DFU人口的前级分析研究.
- 使用既有工具 (QIPS,PROBAST) 进行独立的文献选,数据提取和偏差风险评估.
主要成果:
- 十一项研究 (13篇论文) 分析了55个ML模型,预测伤口愈合,截肢或死亡率.
- 模型主要使用临床数据和随机森林,大多数实现接收器操作特征曲线 (AUROC) 下面面积≥0.8.8.
- 所有纳入的研究都显示出偏差的高风险,原因是定义不一致,样本规模小,数据处理不充分.
结论:
- 几种ML模型显示了对DFU结果的良好的区分能力.
- 广泛的临床应用受到专注于模型开发,缺乏外部验证和使用无法解释的技术的阻碍.
- 未来的研究应该优先考虑对DFU护理的外部验证和可解释的ML模型的开发.
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