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开发和验证用于预测2型糖尿病患者糖尿病视网膜病变的机器学习算法:算法开发研究

Sunyoung Kim1,2, Jaeyu Park2,3, Yejun Son2,4

  • 1Department of Family Medicine, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, Republic of Korea.

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概括

机器学习模型可以预测2型糖尿病 (T2DM) 的成年人糖尿病视网膜病变 (DR). XGBoost模型显示了早期DR检测和干预的潜力,改善了患者的治疗结果.

关键词:
算法算法是一种算法.这种疾病是共患的.糖尿病视网膜病变 糖尿病视网膜病变机器学习是机器学习.眼科 眼科 眼科预测 预测 预测 预测视网膜上的视网膜.2 型糖尿病 2 型糖尿病

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科学领域:

  • 眼科医生 眼科 眼科
  • 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:

  • 糖尿病视网膜病变 (DR) 是全球可预防失明的主要原因.
  • 机器学习 (ML) 提供了在社区环境中增强DR查的潜力.
  • 在DR查中对ML的预测模型性能需要进一步确定.

研究的目的:

  • 评估基于ML的DR发展的风险预测在2型糖尿病 (T2DM) 的成年人中.
  • 利用韩国医疗保健数据开发一种通用且准确的DR风险预测模型.

主要方法:

  • 利用了来自韩国3所大学医院的电子病历 (发现队列:n=14,694;验证队列:n=1856).
  • 开发和调整各种ML模型,根据接收器操作特征 (ROC) 曲线下的面积选择性能最好的模型.
  • 主要结局:在3岁时出现DR.

主要成果:

  • 极端梯度增强 (XGBoost) 模型在发现队列中达到75.13%的准确性,在验证队列中达到65.14%的准确性.
  • 通过XGBoost识别的关键预测因素包括脂质不良,癌症,高血压,慢性病,神经病变和心血管疾病.
  • 在接受查的患者中,DR诊断为2.37%.

结论:

  • 基于ML的风险预测,特别是使用XGBoost,显示了对T2DM患者及时的DR干预的潜力.
  • 该模型可以提高对DR贡献因素的理解,并减少并发症.
  • 预计拟议的模型将在韩国的初级保健机构中具有成本效益.