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机器学习预测前列腺动脉栓塞结果

G Vigneswaran1,2, N Doshi1,2, D Maclean1

  • 1Department of Interventional Radiology, University Hospital Southampton, Southampton, UK.

Cardiovascular and interventional radiology
|June 19, 2024
PubMed
概括
此摘要是机器生成的。

机器学习模型预测前列腺动脉栓塞 (PAE) 患者的一年结果. 该工具使用手术前数据预测国际前列腺症状评分 (IPSS) 变化,有助于在治疗前向患者提供咨询.

关键词:
人工智能的人工智能是人工智能.血栓形成的过程前列腺前列腺前列腺

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科学领域:

  • 泌尿器科 泌尿器科 泌尿器科 泌尿器科
  • 医疗信息学 医疗信息学
  • 机器学习 机器学习

背景情况:

  • 前列腺动脉栓塞 (PAE) 是一种治疗良性前列腺增生的方法.
  • 预测PAE后患者的结果对于治疗规划和患者咨询至关重要.
  • 目前用于预测结果的方法可能无法充分利用程序前数据.

研究的目的:

  • 开发和验证机器学习 (ML) 模型,用于预测PAE后一年的结果.
  • 利用手术前的患者数据预测国际前列腺症状评分 (IPSS) 的变化.
  • 创建一个实时预测的交互工具,以协助患者咨询.

主要方法:

  • 来自英国ROPE注册表和单一机构 (2012-2023) 的数据的回顾性分析.
  • 应用各种ML模型 (线性回归,拉索,,决策树,随机森林) 进行交叉验证.
  • 使用临床和泌尿动力学参数预测基线IPSS和1年变化.

主要成果:

  • 机器学习模型在预测基线IPSS和一年后的变化方面表现出合理的表现.
  • 基线IPSS预测的平均绝对误差在4.9-7.3.3之间.
  • 预测基线IPSS的模型错误与1年的IPSS变化有显著的相关性 (R2=0.2,p<0.001).

结论:

  • 机器学习模型可以有效地预测PAE后一年的IPSS改进.
  • 一个集成的,用户友好的数字界面允许实时预测结果.
  • 这种预测工具可以增强治疗前的患者咨询,并为临床决策提供信息.