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相关概念视频

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...
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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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.
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Actuarial Approach01:20

Actuarial Approach

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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,...
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Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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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,...
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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The Effect of Neighborhood Deprivation on Mortality in Newly Diagnosed Diabetes Patients: A Countrywide Population-Based Korean Retrospective Cohort Study, 2002-2013.

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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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对使用深度学习方法的老年社区成员的长期生存预测模型.

Kyoung Hee Cho1, Jong-Min Paek2, Kwang-Man Ko2

  • 1Department of Health Policy and Management, SangJi University, Wonju-si 26339, Republic of Korea.

Geriatrics (Basel, Switzerland)
|October 27, 2023
PubMed
概括

这项研究开发了一种深度学习模型,用于预测老年人的生存率,确定关键的风险因素,如并发症和脆弱性,以改善健康管理和寿命.

关键词:
社区生活的老年人.伴随性疾病发生率.深度学习是一种深度学习.脆弱 脆弱 脆弱 脆弱 脆弱生存预测模型的生存预测模型.

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科学领域:

  • 老年学是一门学科.
  • 医疗保健中的人工智能
  • 公共卫生 公共卫生

背景情况:

  • 人口老龄化需要健康老龄化和经济参与的战略.
  • 预测生存率和识别健康风险对于老年人来说至关重要.

研究的目的:

  • 开发一种深度学习模型,用于预测社区老年人的生存时间.
  • 识别和量化各种风险因素对生存期的影响.

主要方法:

  • 使用的韩国国民健康保险服务对189,697名66岁的个人在11年 (2009-2019) 期间的索赔数据.
  • 开发并验证了一种基于深度学习的生存时间预测模型 (C-统计 = 0.7011).

主要成果:

  • 确定了重要的生存预测因素:查尔森并发症指数,虚弱指数,长期护理等级,残疾等级,收入,糖尿病/高血压/脂质失调综合,性别,吸烟和酒精消费.
  • 查尔森的并发症指数和脆弱性指数显示出最强的预测能力 (SHAP值分别为0.0445和0.0443).

结论:

  • 深度学习模型可以有效地预测老年人的生存率.
  • 确定可修改的风险因素 (例如,并发症,脆弱性),可以为长寿提供个性化的健康管理策略.