预测慢性病的进展:对人工智能和机器学习方法的系统审查
Fizza Khalid1, Lara Alsadoun2, Faria Khilji3,4
1Nephrology, Sharif Medical City Hospital, Lahore, PAK.
Cureus
|June 12, 2024
概括
人工智能和机器学习在预测慢性病 (CKD) 进展方面表现有前途. 这些AI/ML模型可以帮助早期检测和针对慢性病患者的个性化干预.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 慢性病 (CKD) 是一种进展性疾病,具有重大公共卫生影响.
- 及时监测和干预对于控制CKD进展至关重要.
- 需要预测模型来识别患有CKD进展高风险的患者.
研究的目的:
- 系统地审查人工智能 (AI) 和机器学习 (ML) 的应用,以预测CKD进展.
- 评估AI/ML模型在CKD预后中的性能和关键因素.
主要方法:
- 进行了系统的文献搜索,以确定使用AI/ML进行CKD进展预测的研究.
- 分析了各种AI/ML算法,包括后勤回归,SVM,随机森林,神经网络和深度学习.
- 研究的重点是预测进展到末期病 (ESRD),替代疗法或估计膜过率 (GFR) 的下降.
主要成果:
- 人工智能/ML模型展示了有希望的预测性能,实现了高精度,灵敏度和特异性.
- 关键预测因素包括纵向数据,基线特征以及EGFR,蛋白尿,白蛋白和血红蛋白等生物标志物.
- 模型成功地预测了CKD进展到ESRD和其他不良结果.
结论:
- 人工智能/ML技术在早期检测,风险分层和个性化治疗CKD方面具有显著的潜力.
- 与电子健康记录的整合可以增强对及时干预的临床决策支持.
- 进一步的研究,验证和伦理考虑对于临床实施至关重要.
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