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

Burn Injuries01:22

Burn Injuries

2.8K
Burn injuries occur when the skin and underlying tissues are damaged due to exposure to heat, electricity, chemicals, radiation, or friction. They can vary in severity, from minor superficial burns to severe deep burns that can be life-threatening.
The damage results in the death of skin cells, which can lead to a massive loss of fluid. Dehydration, electrolyte imbalance, and renal and circulatory failure follow, which can be fatal. Burn patients are treated with intravenous fluids to offset...
2.8K

You might also read

Related Articles

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

Sort by
Same author

The effect of video assisted instruction on central venous catheter application skills, anxiety and satisfaction in nursing students: a randomised controlled study.

BMC medical education·2026
Same author

Centrifugal microfluidics for rapid target analyte quantification in airborne bioaerosols.

Lab on a chip·2026
Same author

Skin-interfaced microfluidic capsule and portable lab-on-a-disc platform for sweat-based monitoring of prenatal nutrient balance.

Nature biomedical engineering·2026
Same author

Prediction of transient hypocalcemia following total thyroidectomy using machine learning methods.

Current problems in surgery·2026
Same author

Comparison of open and laparoscopic surgical techniques in colorectal cancer surgery: Early and late results.

Medicine·2026
Same author

Prevention of ventilator-associated pneumonia: intensive care nurses' perspectives and solution-oriented recommendations-a qualitative study.

BMC nursing·2026

Related Experiment Video

Updated: Sep 11, 2025

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

8.3K

Machine Learning Models for Predicting In-Hospital Mortality in Burn Patients.

Samet Şahin1, Burak Yavuz2, Onur Karaca3

  • 1Department of General Surgery, Muğla Sıtkı Koçman University, 48000 Muğla, Turkey.

Annali Italiani Di Chirurgia
|August 18, 2025
PubMed
Summary

Machine learning models accurately predict in-hospital mortality in burn patients. Logistic Regression and Random Forest show strong potential for improving clinical decision-making in burn care.

Keywords:
burns/mortalitymachine learningrisk assessment

More Related Videos

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
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

634

Related Experiment Videos

Last Updated: Sep 11, 2025

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

8.3K
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
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

634

Area of Science:

  • Medical Informatics
  • Computational Biology
  • Burn Care Research

Background:

  • In-hospital mortality prediction in burn patients is crucial for effective clinical management.
  • Machine learning (ML) offers advanced analytical capabilities for complex health data.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting in-hospital mortality in burn patients.
  • To identify key predictors of mortality in this patient population.

Main Methods:

  • A retrospective cohort study analyzed data from 218 burn patients (2015-2020).
  • Seven ML models (Logistic Regression, Random Forest, SVM, Decision Tree, KNN, Naive Bayes, Gradient Boosting) were trained and evaluated.
  • Key variables included demographics, burn characteristics, and inflammatory markers.

Main Results:

  • The overall in-hospital mortality rate was 18.8%.
  • Logistic Regression achieved the highest ROC-AUC (0.906), while Random Forest demonstrated the highest accuracy (90.9%) and recall (97.2%).
  • K-Nearest Neighbors showed superior recall (99.0%).

Conclusions:

  • Machine learning models, especially Logistic Regression and Random Forest, are effective in predicting burn patient mortality.
  • These findings support the use of ML for data-driven prognosis and personalized treatment in burn care.
  • Multicenter validation is recommended for broader applicability.