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

Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

2.6K
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
2.6K
Three-Compartment Open Model01:06

Three-Compartment Open Model

1.2K
The three-compartment open model is a pharmacokinetic model used to describe the distribution and elimination of drugs following extravascular administration. It comprises a central compartment representing the plasma and two peripheral compartments. The highly perfused peripheral compartment represents organs and tissues with a rich blood supply, such as the liver, kidneys, and lungs. The scarcely perfused peripheral compartment represents tissues with lower blood supply, such as adipose...
1.2K
Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

97
PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure...
97
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

779
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:
779

You might also read

Related Articles

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

Sort by
Same author

Machine learning-enabled label-free SERS for microbial sensing: Toward robust, generalizable, and deployable workflows.

Talanta·2026
Same author

Acute oral toxicity and systemic pathological effects of methiopropamine in female ICR mice.

Regulatory toxicology and pharmacology : RTP·2026
Same author

Machine learning algorithms in the estimation of sex from 3DCT-generated cranial and pelvic measurements.

International journal of legal medicine·2026
Same author

Revolutionizing Transcriptomics: From Single-Cell Insights to RNA-based Interventions.

SLAS technology·2026
Same author

Pre-Post Evaluation of Documentation Burden, Time Perception, and Cognitive Workload of Ambient AI Scribe Tools.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same author

Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) Dysfunction in Human Diseases: Molecular Mechanisms and Pathophysiological Implications.

Cells·2026

Related Experiment Video

Updated: May 4, 2026

A Reproducible Intensive Care Unit-Oriented Endotoxin Model in Rats
05:56

A Reproducible Intensive Care Unit-Oriented Endotoxin Model in Rats

Published on: February 20, 2021

2.0K

Machine learning-based prognostic model for 30-day mortality prediction in Sepsis-3.

Md Sohanur Rahman1, Khandaker Reajul Islam2, Johayra Prithula1

  • 1Department of Electrical and Electronics Engineering, University of Dhaka, Dhaka, 1000, Bangladesh.

BMC Medical Informatics and Decision Making
|September 9, 2024
PubMed
Summary

This study developed a stacking classifier to predict 30-day mortality in Sepsis patients, achieving high accuracy. The model aids in early intervention for improved Sepsis patient outcomes.

Keywords:
30-day mortality predictionMachine learningPrognostic modelSepsisStacking-based meta-classifier

More Related Videos

Evaluation of a Reliable Biomarker in a Cecal Ligation and Puncture-Induced Mouse Model of Sepsis
05:28

Evaluation of a Reliable Biomarker in a Cecal Ligation and Puncture-Induced Mouse Model of Sepsis

Published on: December 9, 2022

3.4K
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

149

Related Experiment Videos

Last Updated: May 4, 2026

A Reproducible Intensive Care Unit-Oriented Endotoxin Model in Rats
05:56

A Reproducible Intensive Care Unit-Oriented Endotoxin Model in Rats

Published on: February 20, 2021

2.0K
Evaluation of a Reliable Biomarker in a Cecal Ligation and Puncture-Induced Mouse Model of Sepsis
05:28

Evaluation of a Reliable Biomarker in a Cecal Ligation and Puncture-Induced Mouse Model of Sepsis

Published on: December 9, 2022

3.4K
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

149

Area of Science:

  • Medical informatics
  • Machine learning in healthcare
  • Critical care medicine

Background:

  • Sepsis is a life-threatening condition in hospitalized patients, especially in the Intensive Care Unit (ICU).
  • Early identification and prediction of Sepsis mortality are critical for improving patient survival rates.
  • Machine learning models offer advanced capabilities for outcome prediction compared to traditional methods.

Purpose of the Study:

  • To develop and validate a prognostic model for predicting 30-day mortality risk in Sepsis-3 patients.
  • To utilize a Stacking-based Meta-Classifier for enhanced prediction accuracy.
  • To analyze the MIMIC-III database for Sepsis patient data.

Main Methods:

  • A cohort of 4,240 Sepsis-3 patients was analyzed, with 783 deaths within 30 days.
  • Fifteen key biomarkers were selected using feature ranking methods (XGBoost, Random Forest, Extra Tree).
  • A stacking-based meta-classifier, incorporating Logistic Regression, was employed for mortality prediction after dataset balancing (SMOTE-TOMEK LINK) and validated using cross-validation.

Main Results:

  • The developed stacking classifier model demonstrated high performance, achieving 95.52% accuracy, 95.79% precision, 95.52% recall, 93.65% specificity, and a 95.60% F1-score.
  • The Logistic Regression classifier within the stacking ensemble achieved an Area Under the Curve (AUC) of 0.99.
  • A nomogram was generated to provide clinical insights into the significance of individual biomarkers.

Conclusions:

  • The proposed stacking classifier, combined with a nomogram, effectively predicts 30-day mortality in Sepsis patients.
  • This predictive model shows significant potential for facilitating early intervention strategies.
  • The findings suggest a promising approach for improving treatment outcomes in Sepsis management.