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基于机器学习的慢性血病预测模型:采用临床和实验室数据的多特征方法.

Rong Wang1,2, Bin Niu1,2, Chenming Zhang1,3

  • 1Department of Infectious Diseases, The First Hospital of Shanxi Medical University, Taiyuan, China.

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概括

机器学习模型可以使用常规数据预测慢性血病的进展. 随机森林模型显示出最佳表现,有助于早期分层对人类血病 (HB) 的风险.

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菌病是什么? 菌病是什么?慢性进展 慢性进展机器学习是机器学习.风险预测风险预测风险分层的风险分层.

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科学领域:

  • 传染性疾病 传染性疾病
  • 机器学习在医学中的应用
  • 临床预测模型临床预测模型

背景情况:

  • 慢性进展影响了近三分之一的人类血病 (HB) 患者,导致长期残疾.
  • 缺乏可靠的早期预测工具阻碍了及时的风险分层和HB的个性化管理.
  • 这项研究解决了使用常规可用的临床和实验室数据进行预测工具的需求.

研究的目的:

  • 开发和验证机器学习 (ML) 模型,用于预测人类血病的慢性进展.
  • 确定慢性血病发展的关键临床和实验室预测因素.
  • 创建一个基于网络的工具,用于慢性血病的早期风险分层.

主要方法:

  • 追溯分析了555名确诊的乳病患者的临床和实验室数据.
  • 使用Boruta进行特征选择和递归特征消除.
  • 使用歧视,校准和临床实用性指标构建和评估六个监督的ML模型 (RF,LightGBM,XGBoost,LR,MLP,SVM). 谢普利添加式解释 (SHAP) 为了解释性.

主要成果:

  • 25.9%的患者进展为慢性血病.
  • 慢性病例显示出不同的生化特征 (例如,较低的ALT,AST,TG;较高的HDL-C,ALB,BUN,UA).
  • 随机森林 (RF) 模型实现了最高的AUC (0.782) 并表现出强大的表现,确定TG,HDL-C,UA,乙酸细胞数量,PA,ALT,BUN和GLB作为关键预测因素.

结论:

  • 采用八个常规变量的RF模型,为慢性血病风险提供了适度的歧视和精确校准的概率估计.
  • 当与临床判断相结合时,开发的工具可能有助于早期风险分层.
  • 在多中心,前性研究中的外部验证对于确认模型的预测性能至关重要.