呼吸系统医学中的模糊逻辑:对预测和诊断应用的系统审查
Troy Kettle1, Tricia M McKeever1, Sherif Gonem2
1NIHR Nottingham Biomedical Research Centre, School of Medicine, University of Nottingham, Nottingham, UK.
International journal of medical informatics
|February 17, 2026
概括
模糊逻辑在呼吸道诊断和预测中显示出透明AI的前景. 然而,方法上的局限性和不充分的验证阻碍了其临床使用,需要进一步的研究.
科学领域:
- 呼吸系统医学 呼吸系统医学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 人工智能 (AI) 越来越多地用于呼吸道诊断和风险预测.
- 与深度神经网络相比,模糊逻辑提供了一个透明和可解释的AI方法.
- 对呼吸系统预测结果中的模糊逻辑应用进行了系统审查.
研究的目的:
- 系统地审查模糊逻辑在呼吸系统医学中用于预测结果和诊断的应用.
- 评估呼吸系统应用中的模糊逻辑系统的性能和识别其局限性.
主要方法:
- 在PubMed和IEEE Xplore中搜索了关于呼吸系统结果预测和诊断中的模糊逻辑研究.
- 包括1998年至2024年间发表的29项研究,涵盖了喘,睡眠呼吸暂停和肺癌等疾病.
- 使用PROBAST评估偏差风险,并由于研究异质性进行了叙事综合.
主要成果:
- 模糊逻辑系统,特别是Mamdani类型,应用于各种呼吸道疾病.
- 性能指标 (灵敏度/特异性) 差异很大 (69-100%和19-100%).
- 在使用明确的临床变量研究中观察到高精度 (>95%),但69%的研究由于验证不良而存在高偏差风险.
结论:
- 模糊逻辑为呼吸预测和诊断提供了一个透明的AI替代方案.
- 临床实施目前受到方法问题和不充分的验证的限制.
- 未来的研究应该专注于前性验证和标准化报告,以推进呼吸系统医学中的模糊逻辑.
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