Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Urinary Tract Calculi VI: Surgical Management01:25

Urinary Tract Calculi VI: Surgical Management

1.0K
Procedures for Kidney StonesMedical intervention is necessary when kidney stones or renal calculi are too large to pass spontaneously (typically greater than 5 millimeters) when stones are accompanied by symptomatic infection (such as fever or pyelonephritis), when they impair kidney function, or when they cause persistent symptoms like severe pain, nausea, or urinary retention. Additionally, patients with only one kidney or those who cannot be treated with medical management also require...
1.0K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Commentary: Beyond the Apnea-Hypopnea Index in the Interpretation of the Obstructive Sleep Apnea-Overactive Bladder Relationship.

International urogynecology journal·2026
Same author

Divergent Radiomolecular Phenotype of Spindle Cell Lipoma​​​​​: Intense 68Ga-PSMA Uptake in a Lesion With Mild 18F-FDG Avidity.

Clinical nuclear medicine·2026
Same author

Machine Learning-Based Prediction of Sperm Retrieval Outcomes in Patients With Klinefelter Syndrome: A Multicenter Study With External Validation.

Andrology·2026
Same author

The histopathological and molecular protective effects of sinapic acid against lead acetate-induced cardiac damage via NRF2/HO-1 and ER stress-related pathways.

Journal of trace elements in medicine and biology : organ of the Society for Minerals and Trace Elements (GMS)·2026
Same author

First-pass reperfusion after endovascular thrombectomy: a real-world analysis with explainable machine learning for intra-procedural decision support.

Journal of neuroradiology = Journal de neuroradiologie·2026
Same author

Evaluation of Screw Loosening in Patients Undergoing Semi-rigid Stabilization with Polyetheretherketone (PEEK) Rods.

Indian journal of orthopaedics·2026

相关实验视频

Updated: May 3, 2026

Vessel-sparing Excision and Primary Anastomosis
08:09

Vessel-sparing Excision and Primary Anastomosis

Published on: January 7, 2019

11.4K

机器学习算法预测了经尿道切除前列腺切除后的尿道狭窄.

Emre Altıntaş1, Ali Şahin2, Huseyn Babayev3

  • 1Faculty of Medicine, Department of Urology, Selcuk University, Tıp Fakültesi Alaeddin Keykubat Yerleşkesi Selçuklu, Konya, 42131, Turkey. dr.e.altintas@gmail.com.

World journal of urology
|May 15, 2024
PubMed
概括
此摘要是机器生成的。

机器学习模型使用手术前血液测试准确预测通过尿道切除前列腺切除 (TURP) 后尿道狭窄风险. 随机森林实现了最高的准确性,显示出临床应用的前景.

关键词:
机器学习,血液参数通过尿道切除前列腺.尿道狭窄是因为尿道狭窄

更多相关视频

Urethral Stricture Induction Followed by Buccal Mucosa Graft Urethroplasty in a Rat Model
05:09

Urethral Stricture Induction Followed by Buccal Mucosa Graft Urethroplasty in a Rat Model

Published on: April 28, 2023

942
Iatrogenic Injury Recapitulated: Electroexcision Technique for Urethral Stricture Modeling in Rats
03:37

Iatrogenic Injury Recapitulated: Electroexcision Technique for Urethral Stricture Modeling in Rats

Published on: October 11, 2024

339

相关实验视频

Last Updated: May 3, 2026

Vessel-sparing Excision and Primary Anastomosis
08:09

Vessel-sparing Excision and Primary Anastomosis

Published on: January 7, 2019

11.4K
Urethral Stricture Induction Followed by Buccal Mucosa Graft Urethroplasty in a Rat Model
05:09

Urethral Stricture Induction Followed by Buccal Mucosa Graft Urethroplasty in a Rat Model

Published on: April 28, 2023

942
Iatrogenic Injury Recapitulated: Electroexcision Technique for Urethral Stricture Modeling in Rats
03:37

Iatrogenic Injury Recapitulated: Electroexcision Technique for Urethral Stricture Modeling in Rats

Published on: October 11, 2024

339

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:

  • 过尿道前列腺切除后 (TURP) 尿道狭窄是一种并发症.
  • 对这种并发症的预测模型对于患者管理至关重要.

研究的目的:

  • 开发和评估用于预测TURP后尿道狭窄的机器学习算法.
  • 用手术前的血液参数作为预测特征.

主要方法:

  • 对109名接受双相TURP治疗的患者进行了回顾性分析.
  • 使用手术前血液测试和患者特征开发机器学习模型.
  • 使用准确度,F1得分和ROC AUC等指标进行性能评估.

主要成果:

  • 观察到血小板分布宽度,平均血小板体积,血小板位数,激活的部分血小板质量时间和血小板质量时间的统计学上显著的手术前差异.
  • 随机森林模型实现了最高的预测准确度 (0.91).
  • 其他模型的准确性各不相同:支持向量机 (0.86),决策树 (0.82),后勤回归 (0.82),K-最近邻居 (0.82),和天真贝斯 (0.77).

结论:

  • 机器学习模型在预测TURP后尿道狭窄方面表现出很高的准确性.
  • 手术前的血液参数是有价值的预测指标.
  • 结合额外变量的未来研究可能会进一步提高预测精度.