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相关概念视频

Diabetic Nephropathy01:28

Diabetic Nephropathy

Definition Diabetic nephropathy is a chronic kidney complication that results from prolonged hyperglycemia.Prevalence It is the most common cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD) worldwide, affecting up to half of individuals with diabetes.Pathophysiology • Sustained hyperglycemia triggers multiple hemodynamic and metabolic changes in the kidney. • Early in the disease, increased renal blood flow and glomerular hyperfiltration occur due to afferent arteriolar...

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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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集成机器学习和深度学习用于预测糖尿病病模型的构建,验证和解释性.

Junjie Ma1, Shaoguang An1, Mohan Cao1

  • 1Department of Clinical Medicine, Bengbu Medical University, Bengbu, China.

Endocrine
|February 23, 2024
PubMed
概括

这项研究开发了一种用于早期糖尿病病 (DN) 诊断的机器学习模型. 随机森林模型表现出卓越的预测性能,有助于临床查.

关键词:
临床预测模型的临床预测模型.糖尿病脏病 糖尿病脏病可以解释性 解释性机器学习是机器学习.

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科学领域:

  • 腎臟病學 (nephrology) 是一種醫學專業.
  • 人工智能的人工智能
  • 医疗信息学 医疗信息学

背景情况:

  • 糖尿病病 (DN) 是糖尿病的一个重大并发症,需要早期和准确的诊断工具.
  • 目前的诊断方法可能具有侵入性或缺乏早期检测的敏感性.

研究的目的:

  • 开发和验证基于机器学习的风险预测模型,用于糖尿病病 (DN) 的辅助诊断.
  • 通过内部和外部验证来评估模型的预测性能和通用性.

主要方法:

  • 使用LASSO,RFE和MRMR等算法进行数据预处理和特征选择.
  • 构建和评估十个机器学习模型,其中随机森林被认为是最佳的.
  • 使用包括ROC,PR,准确性,MCC和Kappa在内的指标进行内部和外部验证.

主要成果:

  • 为DN预测确定了15个关键变量.
  • 随机森林模型在测试组中表现出卓越的预测性能,ROC为0.912.
  • 外部验证队列显示强烈的概括,ROC值为0.828和0.863.

结论:

  • 开发的机器学习模型证明了糖尿病病的显著预测价值.
  • 该模型准备协助早期诊断和临床查DN.
  • 基于该模型的在线平台可促进临床应用的可访问性.