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相关概念视频

Kaplan-Meier Approach01:24

Kaplan-Meier Approach

129
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,...
129
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

219
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...
219
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

177
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...
177
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

122
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.
122
Actuarial Approach01:20

Actuarial Approach

74
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,...
74

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相关实验视频

Updated: Jun 24, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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对于具有可解释机器学习的门诊患者,不满意度考虑的等待时间预测.

Jongkyung Shin1, Donggi Augustine Lee2, Juram Kim3

  • 1Graduate School of Artificial Intelligence, Ulsan National Institute of Science and Technology, 50 Unist-gil, Eonyang-eup, Ulju-gun, 44919, Ulsan, Republic of Korea.

Health care management science
|June 1, 2024
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概括

这项研究引入了一个新的框架,可以准确预测门诊等待时间,防止低估和减少患者不满. 具有不对称损失函数和新型错误得分的机器学习模型提供了更好的等待时间估计和解释.

关键词:
不对称的损失函数的功能.可以解释的机器学习.门诊服务 门诊服务患者的不满症患者的不满.预测等待时间预测

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科学领域:

  • 医疗保健服务研究 医疗服务研究
  • 医疗保健中的机器学习
  • 运营研究 运营研究

背景情况:

  • 门诊病房的长时间等待大大导致了患者的不满.
  • 准确预测等待时间对于管理患者期望和提高医疗保健运营效率至关重要.
  • 现有的预测模型可能无法充分解决低估等待时间的后果.

研究的目的:

  • 开发和验证一个新的框架来估计门诊等待时间,明确考虑患者的不满.
  • 通过防止低估,提高等待时间预测的准确性和可靠性.
  • 为患者和医疗保健管理人员提供预测等待时间的解释性解释.

主要方法:

  • 实施一个机器学习框架,利用不对称的损失函数来更严重地惩罚低估.
  • 建议和应用一个不满意度意识不对称错误得分 (DAES) 进行最佳的模型选择.
  • 利用沙普利增量解释 (SHAP) 来解释模型预测,并确定影响等待时间的关键因素.

主要成果:

  • 拟议的框架有效地通过使用不对称损失函数来防止低估等待时间.
  • DAES提供了一种平衡的模型选择方法,优化了准确性和低估之间的权衡.
  • SHAP分析揭示了关键的操作因素,如队列长度,作为等待时间的主要决定因素,使可操作的见解成为可能.

结论:

  • 开发的框架为预测门诊等待时间提供了强大的解决方案,大大减少了患者的不满.
  • 不对称损失函数和DAES的整合提高了预测准确性和模型可解释性.
  • 这种方法有助于在医院实践应用实时患者通知和医疗保健服务的整体运营改进.