Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Strategies for Assessing and Addressing Confounding

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...

You might also read

Related Articles

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

Sort by

Same author

Digital biomarkers in non-communicable diseases.

Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals·2026

Same author

Organizational Tensions in the Implementation of Modifiable Off-the-Shelf Technologies in a University Hospital: Qualitative Multimethod Study.

JMIR human factors·2026

Same author

The predicted body weight equation overestimates lung sizes of female, critically ill patients: an analysis of randomized, controlled trials and real-world clinical data.

Intensive care medicine·2026

Same author

The Patient Monitoring Roundtable as Catalyst for Health Care Innovation: Case Study.

Journal of participatory medicine·2026

Same author

The Impact of Evaluation Strategy on Sepsis Prediction Model Performance Metrics in Intensive Care Data: Retrospective Cohort Study.

Journal of medical Internet research·2026

Same author

Disclaimers and Referral Patterns for Medical Advice Across Urgency Levels: Large Language Model Evaluation Study.

Journal of medical Internet research·2026

Same journal

How Does That Large Language Model Make You Feel?

Journal of medical Internet research·2026

Same journal

Transformation Versus Innovation in Digital Health Care and the Future of Clinical AI.

Journal of medical Internet research·2026

Same journal

Building a Malaria Intelligence System for Real-Time Prediction and Data-Driven Intervention Planning.

Journal of medical Internet research·2026

Same journal

Therapeutic Interaction Features of AI Chatbots in Depression Interventions: Systematic Review and Meta-Analysis.

Journal of medical Internet research·2026

Same journal

Large Language Model Versus Multidisciplinary Team: Feasibility Study of Pancreatic Cancer Management Recommendations.

Journal of medical Internet research·2026

Same journal

Centers for Medicare & Medicaid Services to Launch Landmark ACCESS Program.

Journal of medical Internet research·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 23, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Developing a Scalable Annotation Method for Large Datasets That Enhances Alarms With Actionability Data to Increase

Sophie Anne Inès Klopfenstein^1,2, Anne Rike Flint¹, Patrick Heeren^1,3

¹Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany.

Journal of Medical Internet Research

|May 5, 2025

Summary

This summary is machine-generated.

Alarm fatigue in ICUs is reduced by a new method that semi-automatically labels patient monitoring alarms for actionability. This enables faster creation of datasets for machine learning in alarm management.

Keywords:

alarm fatigue alarm informativeness alarm management dataset annotation digital health intensive care unit machine learning patient monitoring patient-centered care technological innovation transdisciplinary research

More Related Videos

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

Related Experiment Videos

Last Updated: May 23, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

Area of Science:

Biomedical Engineering
Health Informatics
Clinical Data Science

Background:

Alarm fatigue in intensive care units (ICUs) stems from frequent nonactionable alarms, impacting patient safety and staff.
Machine learning (ML) models can potentially mitigate alarm fatigue, but require actionable alarm data, which is currently lacking.
Manual annotation of alarm actionability is time-consuming and resource-intensive, hindering ML development.

Purpose of the Study:

To propose a scalable method for annotating patient monitoring alarms based on their actionability.
To enable the creation of datasets for developing ML models to address alarm fatigue.
To provide a reusable method for other institutions and researchers.

Main Methods:

An interdisciplinary team employed a mixed-methods approach, including data-driven, qualitative, and empirical strategies.
A six-step iterative process involved defining terms, establishing consensus, defining conditions, creating mapping tables, developing a rule set, and evaluation.
Decisions were guided by feasibility, clinical relevance, data availability, and system specifics, informed by literature review and system analysis.

Main Results:

A multidisciplinary consensus led to a rule-based annotation method classifying alarms as actionable or nonactionable using patient data.
The method, focusing on respiratory and medication interventions, includes 8 general rules with graphical examples and mapping tables for unstructured data.
The developed resources and annotation method are presented for reuse.

Conclusions:

The proposed method allows for rapid, semi-automatic, retrospective labeling of alarms for actionability.
This facilitates the generation of annotated datasets crucial for ML in alarm management and fatigue research.
The universal nature of the method and resources supports broader applications in future ML projects.