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

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

388
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
388
Prediction Intervals01:03

Prediction Intervals

2.3K
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. 
2.3K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

488
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...
488
Cancer Survival Analysis01:21

Cancer Survival Analysis

402
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...
402
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

293
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...
293
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

160
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.
160

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

A novel inflammatory-nutritional index: NPAR-correlates with the severity of type 2 diabetic foot ulcers.

Frontiers in nutrition·2026
Same author

Graph Network Feature Space Fusion for Predicting Irregularly Sampled Medical Time-Series Data: Deep Learning Model Development and Validation Study.

JMIR medical informatics·2026
Same author

Multimedia ecological risk deciphering and sediment source apportionment of potentially toxic elements in a typical coastal bay: A combined PMF-SOM-GIS approach.

Journal of hazardous materials·2026
Same author

Antibiofilm efficacy of zwitterionic 2-methacryloloxyethyl phosphorylcholine (MPC) polymer coatings against Vibrio fischeri.

Water research·2026
Same author

Echinacoside improves antioxidant responses and angiogenesis through Parkin-MFN2-mediated mitophagy to promote diabetic wound healing.

Free radical biology & medicine·2026
Same author

Interpretable machine learning analysis of routine blood biomarkers and derived indicators for predicting coronary heart disease in patients with carotid stenosis.

BMC cardiovascular disorders·2026

相关实验视频

Updated: Jul 26, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K

使用集体学习和灰狼优化进行COVID-19死亡率预测.

Lihua Lou1, Weidong Xia1, Zhen Sun2

  • 1Department of Burn, Wound Repair and Regenerative Medicine Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.

PeerJ. Computer science
|June 22, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种使用机器学习的计算方法,用于预测COVID-19死亡风险. 该模型准确识别高风险患者,可能降低成本并改善预后.

关键词:
人工智能的人工智能是人工智能.在 COVID-19 疫情中,数据科学是数据科学.组合学习学习 组合学习遗传算法 遗传算法 遗传算法灰狼优化优化 灰狼优化机器学习是机器学习.死亡率 死亡率 死亡率预测 预测 预测

更多相关视频

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

274

相关实验视频

Last Updated: Jul 26, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

274

科学领域:

  • 计算生物学是一种计算生物学.
  • 医疗信息学医学信息学
  • 机器学习在医疗保健中的应用

背景情况:

  • COVID-19呈现出一种严重程度的频谱,从轻度到致命.
  • 准确的风险分层对于最佳的患者管理和治疗决策至关重要.
  • 识别具有严重后果高风险的个体是一个关键的临床需求.

研究的目的:

  • 开发和验证用于估计COVID-19患者死亡风险的计算方法.
  • 利用集体学习和遗传算法创建一个预测模型.
  • 确定准确和有效的风险评估的关键特征.

主要方法:

  • 组合学习模型与遗传算法相结合的开发.
  • 培训和验证使用4,711个已确认的SARS-CoV-2感染的数据集.
  • 使用AUCROC (接收器操作特征曲线下的面积) 评估模型性能.

主要成果:

  • 性能最好的组合模型实现了0.7802.0的AUCROC.
  • 最佳模型只需要10个特征,简化了数据收集.
  • 机器学习和遗传算法组合的强度和效率被证明.

结论:

  • 拟议的计算方法有效估计了COVID-19死亡风险.
  • 该模型的效率 (功能更少) 可以降低诊断成本并改善预后时间表.
  • 机器学习与遗传算法相结合,为开发临床预测模型提供了强大的方法.