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相关概念视频

Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

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A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
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相关实验视频

Updated: May 6, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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炎症指数和机器学习算法用于预测尿路整形成功率.

Emre Tokuc1, Mithat Eksi2, Ridvan Kayar3

  • 1Urology Clinic, Haydarpasa Numune SUAM, University of Health Sciences, Istanbul, Türkiye. emretokuc@gmail.com.

Investigative and clinical urology
|May 7, 2024
PubMed
概括

血液学炎症标志物,特别是泛免疫炎症值 (PIV),可以预测尿道收缩在尿路整形术后的复发. 与传统方法相比,机器学习算法显著提高了预测准确性.

关键词:
人工智能的人工智能是人工智能.生物标志物 生物标志物尿道是什么意思 尿道是什么意思尿道狭窄症是什么意思

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科学领域:

  • 泌尿器科 泌尿器科 泌尿器科 泌尿器科
  • 炎症标记物 炎症标记物
  • 医疗信息学 医疗信息学

背景情况:

  • 在初级尿路整形术后尿道狭窄的复发仍然是一个临床挑战.
  • 血液炎症标志物越来越多地被探索,因为它们在各种条件下具有预后价值.

研究的目的:

  • 评估血液学炎症标志物的预测能力,以评估尿道狭窄性复发后的尿道狭窄性复发.
  • 将传统统计模型的性能与用于预测复发的机器学习算法进行比较.

主要方法:

  • 分析了一组287名接受初级尿路整形手术的患者.
  • 收集的数据包括患者人口统计学,并发症,血液炎症标志物 (例如PLR,SII,PIV) 和收缩特征.
  • 患者被跟踪了一年,以评估复发情况,使用后勤回归和机器学习进行分析.

主要成果:

  • 在复发和非复发组之间观察到狭窄长度,局部和炎症标志物 (PLR,SII,PIV) 的显著差异.
  • 多变量分析确定了收缩长度和PIV作为复发的重要预测因素.
  • 机器学习算法表现出优异的预测性能 (AUC 0.82),与经典后勤回归 (AUC 0.65) 相比.

结论:

  • 全免疫-炎症值 (PIV) 在预测尿道收缩在尿道整形术后的复发方面表现有前途.
  • 机器学习算法为预测尿路整形结果提供了更高的准确性,可能有助于个性化治疗策略和名ogram开发.