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

Cancer Survival Analysis01:21

Cancer Survival Analysis

458
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...
458
Prediction Intervals01:03

Prediction Intervals

2.4K
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. 
2.4K
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

281
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,...
281
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

683
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
683
Actuarial Approach01:20

Actuarial Approach

140
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,...
140
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

339
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
339

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

Length of hospitalization in intensive care units: A cross-sectional study.

Medicine·2026
Same author

Exploring patient satisfaction with community pharmacy services in the United Arab Emirates: Implications for quality improvement.

PloS one·2026
Same author

Integrative metabolomics and proteomics of heat stress.

Clinica chimica acta; international journal of clinical chemistry·2026
Same author

Correction: Insulin-self administration among individuals with diabetes: Implications for improved practices.

PloS one·2026
Same author

Factors influencing the prediction of obstructive sleep apnea: an observational study from a developing country.

Future science OA·2026
Same author

The Effect of Thermal Cycling and Cyclic Loading on The Flexural Strength of Milled PMMA Versus 3D Printed PEEK as a Provisional Restorative Restoration.

Clinical, cosmetic and investigational dentistry·2026

相关实验视频

Updated: Sep 18, 2025

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

777

使用基于XGBoost的评分系统进行ICU死亡率预测:发展中国家的一项研究.

Reema Karasneh1, Sayer Al-Azzam2, Karem H Alzoubi3,4

  • 1Department of Basic Medical Sciences, Faculty of Medicine, Yarmouk University, Irbid, Jordan.

Reviews on recent clinical trials
|June 20, 2025
PubMed
概括

这项研究开发了一种XGBoost模型,用于约旦的重症监护室 (ICU) 死亡率预测. 该模型实现了高准确性,确定住院,白蛋白和尿素水平作为关键预测指标.

关键词:
在ICU中,医生会对患者进行治疗.基于XGBoost的模型核心系统. 核心系统.机器学习是机器学习.死亡率预测死亡率预测

更多相关视频

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

201
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

381

相关实验视频

Last Updated: Sep 18, 2025

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

777
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

201
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

381

科学领域:

  • 密集护理医学 密集护理医学
  • 医疗保健中的机器学习
  • 预测分析是一种预测分析.

背景情况:

  • 在重症监护室 (ICU) 准确的死亡率预测对于患者的治疗结果和资源分配至关重要.
  • 传统的评分系统难以处理复杂的,高维度的ICU数据.
  • 这项研究解决了对约旦人口量身定制的死亡率预测模型的需求.

研究的目的:

  • 开发和评估一个有效的机器学习模型,用于约旦的ICU死亡率预测.
  • 为了比较XGBoost模型与传统方法的性能.
  • 在约旦情况下,确定与ICU死亡率相关的关键因素.

主要方法:

  • 进行了一项单一中心的回顾性队列研究.
  • 使用XGBoost机器学习算法创建了一个新的ICU死亡率预测模型.
  • 使用临床数据评估模型性能,并与后勤回归进行比较.

主要成果:

  • XGBoost模型表现出卓越的性能,准确度为87.91%,灵敏度为92.88%,AUC-ROC为94.29%.
  • 关键预测ICU死亡率的关键预测因素包括住院时间长度,白蛋白水平和尿素水平.
  • SHAP值表明这些因素具有显著的预测能力.

结论:

  • 使用XGBoost成功开发了一个局部适应的ICU死亡率预测模型.
  • 住院时间,白蛋白和尿素水平是约旦ICU患者结果的关键预测指标.
  • 在这种情况下,XGBoost模型为死亡率预测提供了一个高度准确和敏感的工具.