相关实验视频

Updated: Jan 17, 2026

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

8.6K

机器学习模型的评估,以从随机临床试验数据中测量个性化治疗效果,并提供时间到事件的结果

Elvire Roblin, Paul-Henry Cournède, Stefan Michiels

    ArXiv
    |September 22, 2025
    PubMed
    概括

    灵活的机器学习模型可以在临床试验中估计个性化的治疗效果. 这些先进的方法,包括神经网络和随机生存森林,有效地处理复杂的患者数据,以实现个性化医疗.

    更多相关视频

    Constructing and Visualizing Models using Mime-based Machine-learning Framework06:19

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

    Published on: July 22, 2025

    2.3K
    Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index06:55

    Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

    Published on: January 8, 2020

    15.1K

    相关概念视频

    Comparing the Survival Analysis of Two or More Groups01:20

    Comparing the Survival Analysis of Two or More Groups

    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...
    561
    Kaplan-Meier Approach01:24

    Kaplan-Meier Approach

    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,...
    577
    Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

    Mechanistic Models: Compartment Models in Individual and Population Analysis

    Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
    250
    Introduction To Survival Analysis01:18

    Introduction To Survival Analysis

    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...
    752
    Cancer Survival Analysis01:21

    Cancer Survival Analysis

    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...
    650
    Actuarial Approach01:20

    Actuarial Approach

    The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
    Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
    290