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

Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

908
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
908
Guidelines For Measuring Vital Signs01:19

Guidelines For Measuring Vital Signs

2.3K
Following these guidelines can help nurses accurately measure vital signs, assess changes in patient conditions, and provide timely treatment when necessary. Adhering closely to the guidelines ensures the accuracy and reliability of the results.
Before taking a patient's vital signs, a nurse would consider and assess the patient's comfort level and ensure appropriate equipment is available.
2.3K

You might also read

Related Articles

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

Sort by
Same author

A comprehensive inference-time augmentation framework in physiological signals: application to PPG-based AF detection.

Physiological measurement·2026
Same author

From Joint Cognitive Systems to Human-AI Joint Cognitive Systems: A Theory Critique and Application to Obstetric Anesthesia Risk Assessment.

Nursing science quarterly·2026
Same author

DietAI24 as a framework for comprehensive nutrition estimation using multimodal large language models.

Communications medicine·2025
Same author

A machine learning model to predict optimal antibiotic use in hospital medicine patients.

Antimicrobial stewardship & healthcare epidemiology : ASHE·2025
Same author

Physics-informed neural networks for physiological signal processing and modeling: a narrative review.

Physiological measurement·2025
Same author

Leveraging Artificial Intelligence for Digital Symptom Management in Oncology: The Development of CRCWeb.

JMIR cancer·2025

Related Experiment Video

Updated: Nov 8, 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

1.4K

Technical considerations for evaluating clinical prediction indices: a case study for predicting code blue events

Kais Gadhoumi1, Alex Beltran2, Christopher G Scully3

  • 1School of Nursing, Duke University, Durham, NC, United States of America.

Physiological Measurement
|April 26, 2021
PubMed
Summary

New methods for evaluating predictive alert algorithms, like the Modified Early Warning Score (MEWS), improve prediction of in-hospital events by optimizing alarm timeliness and reducing false alarms.

Keywords:
clinical alarmsclinical deteriorationearly warning scoreperformance evaluationpredictive value of testsvital signs

More Related Videos

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.3K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.7K

Related Experiment Videos

Last Updated: Nov 8, 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

1.4K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.3K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.7K

Area of Science:

  • Clinical Informatics
  • Healthcare Predictive Analytics
  • Patient Monitoring Systems

Background:

  • Existing tools for predicting patient health deterioration often lack comprehensive performance evaluations, hindering clinical implementation.
  • Accurate and timely alerts are crucial for effective patient management in critical care settings.
  • The Modified Early Warning Score (MEWS) is a widely used tool, but its performance in predicting severe events like code blue needs further optimization.

Purpose of the Study:

  • To develop and evaluate novel techniques and metrics for assessing the performance of predictive alert algorithms.
  • To analyze the impact of alarm timeliness and clinical burden on the utility of predictive algorithms.
  • To demonstrate the advantages of enhanced evaluation metrics using the Modified Early Warning Score (MEWS) for predicting in-hospital code blue events.

Main Methods:

  • Calculated various implementations of MEWS using physiological data from electronic health records of adult ICU patients.
  • Evaluated MEWS performance using conventional metrics alongside non-conventional metrics focusing on alarm timeliness, practicality, and false alarm burden.
  • Assessed MEWS performance with different time windows (2-hour intervals) and prediction horizons (12 hours prior to an event).

Main Results:

  • MEWS calculated over 2-hour intervals using worst-case measurements reduced the false alarm rate by over 50% (0.19/h to 0.08/h) while maintaining high sensitivity (∼80%).
  • A 12-hour prediction horizon significantly improved specificity (∼60%), precision (∼155%), and the work-up to detection ratio (∼50%), with only a marginal decrease in sensitivity (∼10%).
  • These results highlight the benefits of incorporating timeliness and alarm burden considerations into performance evaluations.

Conclusions:

  • Performance metrics that capture alarm timeliness and burden are essential for understanding the clinical utility of predictive alarm systems.
  • Optimized MEWS implementations can significantly enhance the prediction of critical events like code blue, improving patient safety.
  • The proposed evaluation techniques provide a more comprehensive assessment of predictive algorithms, guiding better clinical practice.