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

Acute Pancreatitis II: Clinical Manifestations and Management01:30

Acute Pancreatitis II: Clinical Manifestations and Management

168
Acute pancreatitis presents a complex medical emergency characterized by rapid onset inflammation of the pancreas, demanding timely diagnosis and management to prevent complications. The condition primarily manifests through severe upper abdominal pain that often radiates to the back. This pain intensifies following the consumption of fatty foods. Accompanying symptoms such as nausea, vomiting, abdominal distention, fever, dyspnea, cyanosis, and jaundice can vary in intensity but significantly...
168
Acute Pancreatitis I: Introduction01:27

Acute Pancreatitis I: Introduction

463
Pancreatitis is inflammation of the pancreas, an organ located behind the stomach. It can be either acute or chronic.
Acute pancreatitis is characterized by rapid inflammation of the pancreas, often caused by factors like gallstone blockage or excessive alcohol consumption. Chronic pancreatitis, on the other hand, is a slow, progressive inflammation that may result from long-term alcohol abuse, obstructions in the pancreatic duct, or genetic factors.
The causes of acute pancreatitis include:
463
Chronic Pancreatitis II: Collaborative Care01:29

Chronic Pancreatitis II: Collaborative Care

123
The management of chronic pancreatitis is multifaceted, involving a comprehensive approach that includes thorough assessment, diagnostic testing, and a variety of management strategies.
Assessment:
123

You might also read

Related Articles

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

Sort by
Same journal

Non-invasive arterial blood pressure waveform generation in critically ill patients: A sensor-based deep learning approach.

Computers in biology and medicine·2026
Same journal

An advanced AI-driven deep learning framework for early detection and precise diagnosis of breast cancer from medical images.

Computers in biology and medicine·2026
Same journal

Corrigendum to "Visual modalities-based multimodal fusion for surgical phase recognition" [Comput. Biol. Med. 166 (2023) 107453].

Computers in biology and medicine·2026
Same journal

Hybrid cellular automaton-based model for quorum sensing-controlled biofilm evolution.

Computers in biology and medicine·2026
Same journal

Isobonducellin phytocompound from Artemisia annua L. plant exhibits anti-colorectal cancer activity via AKT1 regulation: an in-silico study.

Computers in biology and medicine·2026
Same journal

A broken power-law model of heart rate variability spectra in sleep.

Computers in biology and medicine·2026

Related Experiment Video

Updated: Aug 28, 2025

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

Improving mortality prediction in Acute Pancreatitis by machine learning and data augmentation.

M Asad Bin Hameed1, Zareen Alamgir1

  • 1Department of Computer Science, National University of Computer and Emerging sciences (NUCES), Lahore, Pakistan.

Computers in Biology and Medicine
|September 22, 2022
PubMed
Summary

This study enhances acute pancreatitis (AP) mortality prediction using machine learning on augmented datasets. Advanced techniques improved model performance, outperforming traditional methods for early detection and patient care.

Keywords:
Acute PancreatitisGenerative Adversarial Network (GAN)ImputationMIMIC-IIIMIMIC-IVMachine learningMortality predictionVariational auto encoder (VAE)

More Related Videos

Preparing a Mice Model of Severe Acute Pancreatitis via a Combination of Caerulein and Lipopolysaccharide Intraperitoneal Injection
07:38

Preparing a Mice Model of Severe Acute Pancreatitis via a Combination of Caerulein and Lipopolysaccharide Intraperitoneal Injection

Published on: May 10, 2024

757
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

Related Experiment Videos

Last Updated: Aug 28, 2025

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
Preparing a Mice Model of Severe Acute Pancreatitis via a Combination of Caerulein and Lipopolysaccharide Intraperitoneal Injection
07:38

Preparing a Mice Model of Severe Acute Pancreatitis via a Combination of Caerulein and Lipopolysaccharide Intraperitoneal Injection

Published on: May 10, 2024

757
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

Area of Science:

  • Medical Informatics
  • Machine Learning
  • Data Science

Background:

  • Acute Pancreatitis (AP) poses significant mortality risks, necessitating early detection for improved patient outcomes.
  • Existing AP mortality prediction models are limited by data scarcity, missing values, and class imbalance, hindering clinical application.
  • Manual scoring systems are being replaced by advanced machine learning (ML) and data mining for more accurate patient risk stratification.

Purpose of the Study:

  • To develop a robust ML model for early prediction of mortality in Acute Pancreatitis patients.
  • To address data quality issues in public AP datasets, including augmentation, imputation, and class imbalance handling.
  • To evaluate the effectiveness of various data augmentation and imputation techniques for improving AP mortality prediction.

Main Methods:

  • Augmented three large public datasets (MIMIC-III, MIMIC-IV, eICU) to create a comprehensive AP dataset.
  • Employed iterative imputation for handling missing data, outperforming basic and KNN-based methods.
  • Utilized advanced generative models (CTGAN, TVAE) and SMOTE for data upsampling to address class imbalance, comparing various class splits.

Main Results:

  • Iterative imputation proved superior for handling missing values in AP data.
  • Data downsampling yielded misleading results on large test sets; upsampling techniques were more effective.
  • Random Forest (RF) classifier achieved the highest performance (0.702 Fβ, 0.833 recall) on CTGAN-generated data (50:50 split); TVAE also showed comparable results.

Conclusions:

  • Augmenting public datasets and employing advanced imputation and generative techniques significantly improve AP mortality prediction models.
  • CTGAN-based data generation with a 50:50 class split, coupled with a Random Forest classifier, offers a promising approach for early AP mortality detection.
  • The developed methods provide a practical solution to data limitations, paving the way for more reliable clinical deployment in AP patient management.