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

Cancer Survival Analysis01:21

Cancer Survival Analysis

626
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
626
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

531
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
531

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

Single-cell co-mapping reveals relationship between chromatin state and gene expression in early zebrafish development.

eLife·2026
Same author

Hybrid Deep Learning Framework for Sleep Quality Prediction: Integrating Metaheuristic Optimization and Statistical Features.

Brain and behavior·2026
Same author

Recent Advances in Analytical Techniques for Cancer Diagnostics and Therapeutics: Combining State-of-the-Art Technologies for Precision Oncology.

Critical reviews in analytical chemistry·2026
Same author

The H3K36me3 methyltransferase SETD2 contributes to PAF1C interactions with RNA Pol II and is required for neuronal differentiation.

The EMBO journal·2026
Same author

Explainable vision transformer framework for multi-class classification and prognostic interpretation of oral cancer in histopathology images.

Discover oncology·2026
Same author

Bridging classical and neural methods for improved segmentation in mathematical text based images.

Scientific reports·2025

相关实验视频

Updated: Jan 7, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

470

使用多算法机器学习框架对甲状腺癌复发的先进预测建模进行比较研究.

Deepak Thakur1, Tanya Gera2, Vivek Bhardwaj3

  • 1School of Computer Science and Engineering, Lovely Professional University, Phagwara, 144001, Punjab, India.

Scientific reports
|December 30, 2025
PubMed
概括
此摘要是机器生成的。

机器学习可以准确地预测甲状腺癌复发. 随机森林模型实现了98.26%的准确性,帮助临床医生识别高风险患者以进行量身定制的管理.

关键词:
临床数据分析临床数据分析机器学习模型的机器学习模型预测建模的预测建模.随机的森林随机的森林甲状腺癌的复发发生

更多相关视频

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

7.3K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.2K

相关实验视频

Last Updated: Jan 7, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

470
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

7.3K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.2K

科学领域:

  • 在瘤学瘤学.
  • 医疗信息学 医疗信息学
  • 机器学习 机器学习

背景情况:

  • 甲状腺癌复发带来了重大的临床挑战,影响了治疗疗效和患者的长期结果.
  • 预测复发对于优化患者管理和后续策略至关重要.

研究的目的:

  • 评估用于预测甲状腺癌复发的多个机器学习模型.
  • 确定用于早期检测高风险复发病例的最准确模型.

主要方法:

  • 利用了383名甲状腺癌患者的现实数据集.
  • 应用并比较了后勤回归,决策树,随机森林,梯度增强,SVM和KNN模型.
  • 采用分层5倍交叉验证,与GridSearchCV进行嵌套交叉验证,类权重,特征选择 (Chi-square,Gini Importance),SHAP分析和模型校准 (同位素回归).

主要成果:

  • 随机森林分类器实现了最高的预测准确性 (98.26%).
  • 最好的模型表现出强烈的灵敏度和特异性,平均嵌套CV精度约为0.964.
  • 模型校准显著提高了临床决策预测概率的可靠性.

结论:

  • 机器学习框架显示了支持早期识别高风险甲状腺癌复发患者的巨大潜力.
  • 这些预测模型可以帮助临床医生制定个性化的随访和治疗计划.
  • 可解释的AI方法,如SHAP分析,为关键的预测特征提供了洞察力.