Jove
Visualize
联系我们

相关概念视频

Kaplan-Meier Approach01:24

Kaplan-Meier Approach

154
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
154
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

200
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...
200
Cancer Survival Analysis01:21

Cancer Survival Analysis

357
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...
357

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

Semen RNA-Based Biomarkers for Prostate Cancer Detection and Risk Stratification: A Prospective Multicenter Validation Study.

The Journal of urology·2026
Same author

Prolonged Tele-Critical Care Utilization Is Associated With Improved ICU Outcomes: Evidence From Veterans Affairs Hospitals.

Critical care medicine·2025
Same author

Predicting Early Deterioration in Lower Acuity Telehealth Patients Using Gradient Boosting.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Prediction of Intensive Care Length of Stay for Surviving and Nonsurviving Patients Using Deep Learning.

Critical care medicine·2025
Same author

Machine learning modelling for predicting the utilization of invasive and non-invasive ventilation throughout the ICU duration.

Healthcare technology letters·2024
Same author

GenHPF: General Healthcare Predictive Framework for Multi-task Multi-source Learning.

IEEE journal of biomedical and health informatics·2023
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

相关实验视频

Updated: Jul 11, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

机器学习用于对重症监护结果进行基准评估.

Louis Atallah1, Mohsen Nabian1, Ludmila Brochini2

  • 1Clinical Integration and Insights, Philips, Cambridge, MA, USA.

Healthcare informatics research
|November 15, 2023
PubMed
概括

机器学习 (ML) 通过改善结果预测来增强重症监护的基准评估. 需要进一步研究重症监护的ML模型中的阶级不平衡,公平性和通用性.

关键词:
基准测试 (benchmarking) 是一种比较的方法.关键的护理关键的护理逗留时间 逗留时间机器学习 机器学习死亡率 死亡率 死亡率透气通风系统的通风方式

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

6.8K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

相关实验视频

Last Updated: Jul 11, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
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
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

科学领域:

  • 关键护理医学 关键护理医学
  • 人工智能的人工智能是人工智能.
  • 医疗信息学 医疗信息学

背景情况:

  • 临床护理的有效性取决于系统的评估和改进.
  • 基准测试,对标准进行比较分析,有助于确定需要改进的领域.
  • 在过去的二十年中,机器学习 (ML) 模型已经在临床结果预测方面取得了先进的进展.

研究的目的:

  • 审查ML中的关键发现和结果,以进行重症监护的基准评估.
  • 引导临床医生和研究人员选择最佳的ML方法.
  • 突出预测临床护理结果的进展,如死亡率,停留时间和机械通风.

主要方法:

  • 使用PubMed和谷歌学者,对2003-2023年的文学进行叙事审查.
  • 搜索了利用ML用于死亡率,停留时间和机械通风的预测模型.
  • 手动策划的文章提供了全面的读者视角.

主要成果:

  • ML有效地解决了关键护理结果预测挑战.
  • 在功能工程,数据预处理,模型选择和验证方面取得的进展.
  • 机器学习模型在处理非线性关系,类不平衡,缺失数据和文档变化方面取得了成功.

结论:

  • ML提供了新的工具来增强重症监护结果的基准评估.
  • 需要在诸如阶级不平衡,公平,校准和通用性等领域进行进一步的研究.
  • 在重症监护中,公布的ML模型的长期验证至关重要.