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

You might also read

Related Articles

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

Sort by
Same author

Improvement of persistent anuria by long-term percutaneous ventricular assist device management and successful bridge to left ventricular assist device implantation in a patient with acute myocardial infarction: a case report.

European heart journal. Case reports·2026
Same author

Lac-Phe elicits anxiolytic-like effects associated with monoaminergic signaling in mice.

Translational psychiatry·2026
Same author

Performance of large language models and prompt engineering strategies for data extraction in systematic reviews.

Frontiers in digital health·2026
Same author

Use of Commercially Available Large Language Models to Generate Information Leaflets on Post-Intensive Care Syndrome: Clinical Utility Assessment.

JMIR formative research·2026
Same author

Astrocytic FABP5 drives non-cell-autonomous oligodendrocyte injury in multiple system atrophy by promoting TNF signaling and ferroptotic stress.

Redox biology·2026
Same author

Minimally Invasive Extirpation of an Eden Type II Dumbbell-Shaped Mediastinal Tumor Using a Posterior and Uniportal Thoracoscopic Approach without Changing the Patient's Position: A Case Report.

Surgical case reports·2026

Related Experiment Video

Updated: Jul 5, 2025

Noninvasive and Invasive Renal Hypoxia Monitoring in a Porcine Model of Hemorrhagic Shock
07:48

Noninvasive and Invasive Renal Hypoxia Monitoring in a Porcine Model of Hemorrhagic Shock

Published on: October 28, 2022

1.2K

Machine-learning model for predicting oliguria in critically ill patients.

Yasuo Yamao1, Takehiko Oami1, Jun Yamabe2

  • 1Department of Emergency and Critical Care Medicine, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo, Chiba, 260-8677, Japan.

Scientific Reports
|January 11, 2024
PubMed
Summary
This summary is machine-generated.

This study developed a machine-learning algorithm to accurately predict oliguria, an early sign of acute kidney injury (AKI), in intensive care unit (ICU) patients. The algorithm shows promise for early AKI diagnosis and improved patient management.

More Related Videos

A Large Animal Model for Acute Kidney Injury by Temporary Bilateral Renal Artery Occlusion
09:02

A Large Animal Model for Acute Kidney Injury by Temporary Bilateral Renal Artery Occlusion

Published on: February 2, 2021

4.5K
Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
09:16

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

6.9K

Related Experiment Videos

Last Updated: Jul 5, 2025

Noninvasive and Invasive Renal Hypoxia Monitoring in a Porcine Model of Hemorrhagic Shock
07:48

Noninvasive and Invasive Renal Hypoxia Monitoring in a Porcine Model of Hemorrhagic Shock

Published on: October 28, 2022

1.2K
A Large Animal Model for Acute Kidney Injury by Temporary Bilateral Renal Artery Occlusion
09:02

A Large Animal Model for Acute Kidney Injury by Temporary Bilateral Renal Artery Occlusion

Published on: February 2, 2021

4.5K
Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
09:16

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

6.9K

Area of Science:

  • Nephrology and Critical Care Medicine
  • Artificial Intelligence in Healthcare
  • Biomedical Informatics

Background:

  • Oliguria, defined as urine output < 0.5 mL/kg/h, is a key indicator of acute kidney injury (AKI).
  • Early prediction of AKI is crucial for timely intervention and improved patient outcomes in intensive care units (ICUs).

Purpose of the Study:

  • To develop and evaluate a machine-learning algorithm for predicting the onset of oliguria in ICU patients.
  • To identify key clinical variables predictive of oliguria using machine learning.

Main Methods:

  • Retrospective cohort study utilizing electronic health record data from 9,241 ICU patients (2010-2019).
  • Development of a predictive model using a light-gradient boosting machine algorithm.
  • Validation of the algorithm's predictive performance using Area Under the Curve (AUC) metrics.

Main Results:

  • The machine-learning algorithm achieved high accuracy in predicting oliguria onset at 6 hours (AUC=0.964) and 72 hours (AUC=0.916).
  • Key predictors identified include urine output, severity scores, serum creatinine, and inflammatory markers like interleukin-6.
  • Oliguria occurred in 27.4% of patients within 6 hours and 30.2% experienced AKI during their ICU stay.

Conclusions:

  • Machine learning offers a powerful tool for accurately predicting oliguria in critically ill patients.
  • Early identification of oliguria through predictive algorithms can facilitate prompt diagnosis and management of AKI.
  • The study highlights the clinical utility of machine learning in enhancing critical care for kidney injury.