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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

602
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
602
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

196
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
196
Cancer Survival Analysis01:21

Cancer Survival Analysis

453
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...
453
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

285
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...
285
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

260
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,...
260
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

395
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
395

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

Transition-metal-free three-component synthesis of α-tertiary trifluoromethyl phosphonates from CF<sub>3</sub> diazo compounds.

Organic & biomolecular chemistry·2026
Same author

Dual Nickel/Photoredox-Catalyzed Radical Phosphonylacylation of Terminal Alkenes with Aroyl Chlorides and <i>H</i>-Phosphine Oxides.

Organic letters·2026
Same author

UHR as a systemic sensor of cumulative heat exposure and subclinical cardiovascular injury: evidence from 64,088 adults.

Frontiers in public health·2026
Same author

Mothership and Drip-and-Ship Strategies in Mechanical Thrombectomy for Acute Ischemic Stroke.

Annals of emergency medicine·2026
Same author

Deep learning model for pathological invasiveness prediction using smartphone-based surgical resection images in clinical stage IA lung adenocarcinoma (SuRImage): a prospective, multicentric, diagnostic study.

The Lancet. Digital health·2026
Same author

Rehabilitation effects of high-intensity interval training on asthma: a systematic review and meta-analysis of randomized controlled trials.

Journal of thoracic disease·2026

相关实验视频

Updated: Sep 10, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.3K

用迪里克莱特分布进行不确定性意识生存分析,用于多尺度病理和基因组学

Songhan Jiang, Linghan Cai, Zhengyu Gan

    IEEE transactions on medical imaging
    |August 22, 2025
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了人工智能生存预测框架,通过模拟患者数据的不确定性来提高准确性. 不确定性意识多模式生存分析 (UMSA) 框架使用病理图像和基因组数据来提高预测.

    更多相关视频

    Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
    06:46

    Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

    Published on: September 27, 2024

    376
    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

    634

    相关实验视频

    Last Updated: Sep 10, 2025

    Establishing a Competing Risk Regression Nomogram Model for Survival Data
    04:57

    Establishing a Competing Risk Regression Nomogram Model for Survival Data

    Published on: October 23, 2020

    10.3K
    Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
    06:46

    Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

    Published on: September 27, 2024

    376
    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

    634

    科学领域:

    • 计算病理学
    • 医学中的人工智能
    • 生物统计学

    背景情况:

    • 数字病理学中的人工智能已经有了先进的生存预测.
    • 目前的生存分析方法往往分辨时间,忽略不确定性和患者异质性.
    • 在生存分析中审查的数据增强了不确定性和可变性.

    研究的目的:

    • 开发一种新的生存分析框架,解决现有方法的局限性.
    • 在生存预测模型中提高不确定性意识.
    • 整合多模式数据,包括病理图像和基因组数据,以改善生存分析.

    主要方法:

    • 使用迪里克莱特分布来建模离散输出作为连续的概率分布,增强不确定性表示.
    • 开发了一种基于不确定性驱动的通用多模式生存分析损失函数.
    • 提出了不确定性意识多模式生存分析 (UMSA) 框架,以分析多尺度病理图像和基因组数据之间的相互作用.

    主要成果:

    • 在生存预测任务中,UMSA框架展示了最先进的性能.
    • 在5个公开数据集的实验评估中验证了拟议方法的有效性和可扩展性.
    • 该方法在生存预测中提供了更准确的不确定性表示.

    结论:

    • 通过纳入不确定性意识,UMSA框架为多模式生存分析带来了重大进步.
    • 这种方法有效地利用病理图像和基因组数据进行更强大的生存预测.
    • 通过提高生存预测准确度,UMSA有望改善临床决策.