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

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

Post-liver transplantation delirium: Pathogenesis, risk factors, clinical management, and future directions.

Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society·2026
Same author

DNA-drug conjugates enable logic-gated drug delivery amplified by hybridization chain reactions.

Nature biotechnology·2026
Same author

Individual-specific functional connectivity predicts clinical symptoms severity in patients with post-traumatic stress disorder.

BMC psychiatry·2026
Same author

Static and dynamic functional connectivity alterations in mice with LPS-induced depression: A 9.4T fMRI study using independent component and graph theory analyses.

Journal of psychiatric research·2026
Same author

Accessory Cavitated Uterine Malformation with Incomplete Septate Uterus.

Journal of minimally invasive gynecology·2026
Same author

Decreased Oxytocin Mediates PVN-CA2 and PVN-PrL in Sleep Deprivation-Induced Social Memory Deficits.

Research (Washington, D.C.)·2026

相关实验视频

Updated: Jun 30, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

通过基于临床参数的机器学习模型进行绝经后子宫内膜非良性病变风险分类.

Jin Lai1, Bo Rao2, Zhao Tian1

  • 1Department of Obstetrics and Gynecology, People's Hospital, Peking University, Beijing, China.

Computers in biology and medicine
|March 14, 2024
PubMed
概括

使用随机森林 (RF) 算法的机器学习模型有效地识别了绝经后患有异常增生症 (AH) 和子宫内膜癌症 (EC) 的女性,达到88.1%的灵敏度和0.93 AUC.

关键词:
人工智能的人工智能是人工智能.宫内膜的病变 宫内膜的病变机器学习 机器学习恶性 恶性 恶性在更年期后的绝经后.

更多相关视频

Author Spotlight: Advancing Reproductive Immunology with a Protocol for the Quantitative Evaluation of Endometrial Immune Cells
07:46

Author Spotlight: Advancing Reproductive Immunology with a Protocol for the Quantitative Evaluation of Endometrial Immune Cells

Published on: October 13, 2023

1.3K
Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors
03:05

Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors

Published on: February 16, 2024

1.0K

相关实验视频

Last Updated: Jun 30, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K
Author Spotlight: Advancing Reproductive Immunology with a Protocol for the Quantitative Evaluation of Endometrial Immune Cells
07:46

Author Spotlight: Advancing Reproductive Immunology with a Protocol for the Quantitative Evaluation of Endometrial Immune Cells

Published on: October 13, 2023

1.3K
Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors
03:05

Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors

Published on: February 16, 2024

1.0K

科学领域:

  • 妇科瘤学 妇科瘤学
  • 医疗信息学 医疗信息学
  • 医疗保健中的机器学习

背景情况:

  • 绝经后子宫内膜病变需要准确的分类来指导临床管理.
  • 区分良性疾病,非典型增生症 (AH) 和子宫内膜癌 (EC) 是至关重要的.

研究的目的:

  • 开发和评估一种机器学习模型,用于对绝经后妇女的非良性子宫内膜病变进行分类.
  • 使用非侵入性临床参数来预测非典型增生症 (AH) 和子宫内膜癌 (EC).

主要方法:

  • 收集了999名绝经后患者的临床数据,确定了57个相关特征.
  • 对比了各种机器学习模型,包括随机森林 (RF),XGBoost和后勤回归.
  • 在152名患者的独立数据集上验证了模型性能,使用AUC,灵敏度和特异性.

主要成果:

  • 随机森林 (RF) 模型获得了最高的性能,测试组的灵敏度为88.1%,AUC为0.93.
  • 与其他评估模型相比,RF显示出对非良性子宫内膜损伤的优越识别能力.
  • 该模型被集成到临床决策支持系统 (CDSS) 中,用于持续验证.

结论:

  • 一个机器学习模型,特别是RF,显示出高分辨能力,用于识别处于风险的绝经后患者.
  • 这种方法为风险分层提供了一种新的策略,有可能改善查和临床干预.
  • 部署的系统有助于持续的性能验证和优化.