Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

232
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
232
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

287
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,...
287
Cancer Survival Analysis01:21

Cancer Survival Analysis

472
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...
472
Survival Tree01:19

Survival Tree

171
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...
171
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

311
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...
311
Actuarial Approach01:20

Actuarial Approach

141
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,...
141

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

SegVir: Reconstruction of Complete Segmented RNA Viral Genomes from Metatranscriptomes.

Molecular biology and evolution·2024
Same author

Nutritional Interventions for the Prevention of Cognitive Decline in Patients With Mild Cognitive Impairment and Alzheimer Disease: Protocol for a Network Meta-Analysis of Randomized Controlled Trials.

JMIR research protocols·2024
Same author

Machine learning-based prediction of COVID-19 mortality using immunological and metabolic biomarkers.

BMC digital health·2023
Same author

Non-steroidal anti-inflammatory drug target gene associations with major depressive disorders: a Mendelian randomisation study integrating GWAS, eQTL and mQTL Data.

The pharmacogenomics journal·2023
Same author

Genetic evidence for causal relationships between age at natural menopause and the risk of ageing-associated adverse health outcomes.

International journal of epidemiology·2022
Same author

Canary: an automated tool for the conversion of MaCH imputed dosage files to PLINK files.

BMC bioinformatics·2022

Related Experiment Video

Updated: Sep 26, 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

892

Identifying Predictors of COVID-19 Mortality Using Machine Learning.

Tsz-Kin Wan1, Rui-Xuan Huang1, Thomas Wetere Tulu2,3

  • 1Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China.

Life (Basel, Switzerland)
|April 23, 2022
PubMed
Summary

This study developed a COVID-19 mortality risk prediction model using machine learning. Key predictors identified include age, lifestyle, illness, income, and family disease history for better COVID-19 mortality assessment.

Keywords:
COVID-19COVID-19 mortalitymachine learning modelmortality predictorsprediction model

More Related Videos

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

8.4K
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

7.0K

Related Experiment Videos

Last Updated: Sep 26, 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

892
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

8.4K
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

7.0K

Area of Science:

  • Computational epidemiology
  • Public health informatics
  • Machine learning in healthcare

Background:

  • Coronavirus disease 2019 (COVID-19) is a significant global respiratory illness.
  • Factors influencing COVID-19 mortality and its pathogenesis remain incompletely understood.
  • Predictors for COVID-19 mortality are needed to guide clinical and public health strategies.

Purpose of the Study:

  • To investigate COVID-19 mortality in patients with pre-existing conditions.
  • To identify associations between COVID-19 mortality and various morbidities.
  • To develop a machine learning model for predicting COVID-19 mortality risk.

Main Methods:

  • Utilized de-identified data from 113,882 individuals, including 14,877 COVID-19 patients, from the UK Biobank.
  • Employed machine learning models: Deep Neural Networks (DNN), Random Forest Classifier (RF), eXtreme Gradient Boosting (XGB), and Support Vector Machine (SVM).
  • Evaluated model performance using the Area Under the Curve (AUC) metric.

Main Results:

  • The Random Forest model demonstrated the highest performance with an AUC of 0.86 (95% CI 0.84-0.88).
  • A risk-level prediction model for COVID-19 mortality was successfully developed.
  • Key predictors of mortality included age, lifestyle factors, pre-existing illnesses, income, and family disease history.

Conclusions:

  • A robust machine learning model can predict COVID-19 mortality risk.
  • Demographic, lifestyle, and health history factors significantly influence COVID-19 outcomes.
  • The findings provide valuable insights for identifying high-risk individuals and informing public health interventions.