预测和诊断慢性病的机器学习方法:当前的趋势,挑战,解决方案和未来的方向
1Department of Computer Science and Engineering, National Institute of Technology Nagaland, Chumukedima, Dimapur, Nagaland, 797103, India. prokashgogoi25@gmail.com.
International urology and nephrology
|November 19, 2024
概括
机器学习 (ML) 通过解决数据限制和隐私问题来推进CKD预测. 本综述强调了生成人工智能用于数据增强和可解释模型,以改善患者的治疗结果.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 科研究 科研究
背景情况:
- 慢性病 (CKD) 构成了全球健康的重大负担,增加了发病率和死亡率.
- 目前在CKD预测中的机器学习 (ML) 应用面临着诸如小数据集和缺乏可解释性等挑战.
- 在处理敏感的CKD数据时,患者的隐私仍然是一个关键问题.
研究的目的:
- 审查目前用于预测和诊断CKD的ML技术.
- 识别和解决关键的研究差距,包括数据增强,特定阶段预测,模型可解释性和数据隐私.
- 探索针对CKD的ML的创新解决方案和未来方向.
主要方法:
- 关于CKD预测中的ML应用的广泛文献调查.
- 分析局限性:数据集小,缺乏特定阶段的预测,解释性差,隐私问题.
- 探索解决方案:生成人工智能,多类模型,SHAP,LIME,同态加密,联合学习和区块链.
主要成果:
- 生成型人工智能可以增强数据集,以改善用于CKD预测的ML模型性能.
- 多类模型对于准确的,特定阶段的CKD预测和量身定制的治疗非常重要.
- 可解释的ML方法 (SHAP,LIME) 提高了临床决策的透明度和信任.
- 保护隐私的技术 (同态加密,联合学习,区块链) 允许安全的数据协作.
结论:
- 解决数据稀缺性,可解释性和隐私对于在CKD预测中推进ML至关重要.
- 实施先进的ML技术可以提高诊断准确度和CKD护理中的患者结果.
- 本审查为未来的研究提供了路线图,用于开发针对CKD的强大,道德和有效的ML解决方案.
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