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相关概念视频

Urinary Bladder01:23

Urinary Bladder

647
The urinary bladder is a hollow, muscular sac that temporarily stores urine before it is expelled from the body. It can hold approximately 600 mL of urine prior to micturition. The bladder is retroperitoneal and located behind the pubic symphysis in the pelvic floor.
In males, the bladder is situated in front of the rectum, while in females, it is positioned anterior to the vagina and uterus. The bladder floor contains an inverted triangular area called the trigone, defined by the two ureteric...
647

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使用基于机器学习的预测进行间歇性囊炎/膀疼痛综合征的风险分类.

Laura E Lamb1, Joseph J Janicki2, Sarah N Bartolone3

  • 1Oakland University William Beaumont School of Medicine, Rochester, MI; Strata Oncology, Ann Arbor, MI.

Urology
|April 27, 2024
PubMed
概括
此摘要是机器生成的。

机器学习通过结合患者报告的结果和尿液生物标志物来改善间歇性囊炎 (IC) 诊断. 新的IC-PIS得分提高了IC/膀疼痛综合征的诊断准确度.

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科学领域:

  • 泌尿器科 泌尿器科 泌尿器科 泌尿器科
  • 生物标志物 生物标志物
  • 机器学习 机器学习

背景情况:

  • 间歇性囊炎 (IC) /膀疼痛综合征 (BPS) 的诊断需要改进.
  • 目前的诊断方法缺乏足够的准确性.
  • 需要新的方法来增强IC/BPS分类.

研究的目的:

  • 使用机器学习开发一个改进的IC风险分类模型.
  • 为了提高IC/BPS的诊断准确度.
  • 创建一个新的分类模型,整合患者报告的结果 (PRO) 和尿液生物标志物.

主要方法:

  • 开发了一种机器学习预测分类模型 (IC-PIS评分).
  • 利用了1264个众筹的尿样和PROs,以及296个学术中心的样本.
  • 测量了尿动细胞因子生物标志物水平,并比较了模型.

主要成果:

  • 最顶级的模型结合了生物标志物测量和PROs,达到0.87.7的AUC.
  • 这种模型的表现超过了单独的PROs (AUC=0.83) 和单独的生物标志物 (AUC=0.58).
  • 生物标志物和PRO的组合为IC带来了更好的预测效应.

结论:

  • IC-PIS是一种新的分类模型,可以提高IC/BPS诊断的准确性.
  • 该模型整合了PRO和尿液生物标志物.
  • 大规模众包数据和环境运输方法支持研究结果的稳定性和可扩展性.