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

Adaptive multi-model ensembles for improved epidemic projections and decision support.

medRxiv : the preprint server for health sciences·2026
Same author

Orchestrator multi-agent clinical decision support system for secondary headache diagnosis in primary care.

Journal of the American Medical Informatics Association : JAMIA·2026
Same author

i2b2-ML: module to facilitate machine learning in the informatics for integrating biology and the bedside platform.

JAMIA open·2026
Same author

Machine Learning Prediction and Reducing Overdoses With Electronic Health Record Nudges (mPROVEN) in the Primary Care Setting: Protocol for a Cluster Randomized Controlled Trial.

JMIR research protocols·2026
Same author

Assessing the Impact of Timing and Coverage of United States COVID-19 Vaccination Campaigns: A Multi-Model Approach.

medRxiv : the preprint server for health sciences·2026
Same author

Stratification of Alzheimer's disease patients using knowledge-guided unsupervised latent factor clustering with electronic health record data.

Communications medicine·2026

Related Experiment Video

Updated: Oct 25, 2025

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing
07:48

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

Published on: April 4, 2025

735

Evaluation of eye tracking for a decision support application.

Shyam Visweswaran1,2, Andrew J King1,3, Mohammadamin Tajgardoon2

  • 1Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.

JAMIA Open
|August 5, 2021
PubMed
Summary

Eye tracking can train machine learning models to identify relevant patient data in electronic medical records (EMR). This novel approach shows promise for developing clinical decision support tools.

Keywords:
electronic medical record systemeye trackingrelevant patient data

More Related Videos

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
10:43

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

5.5K
Using Eye Movements to Evaluate the Cognitive Processes Involved in Text Comprehension
06:49

Using Eye Movements to Evaluate the Cognitive Processes Involved in Text Comprehension

Published on: January 10, 2014

27.5K

Related Experiment Videos

Last Updated: Oct 25, 2025

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing
07:48

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

Published on: April 4, 2025

735
Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
10:43

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

5.5K
Using Eye Movements to Evaluate the Cognitive Processes Involved in Text Comprehension
06:49

Using Eye Movements to Evaluate the Cognitive Processes Involved in Text Comprehension

Published on: January 10, 2014

27.5K

Area of Science:

  • Human-Computer Interaction
  • Medical Informatics
  • Machine Learning

Background:

  • Eye tracking is crucial for understanding user attention and cognition during electronic medical record (EMR) tasks.
  • Clinical decision support tools can enhance healthcare by predicting relevant patient data for specific tasks.

Purpose of the Study:

  • To evaluate eye tracking as a method for generating training data for machine learning-based clinical decision support.
  • To compare the accuracy of eye tracking with manual annotation in identifying physician-relevant patient data within EMRs.

Main Methods:

  • Utilized a low-cost eye-tracking device in a laboratory setting to record physician gaze points during EMR tasks.
  • Developed and evaluated various methods for processing gaze point data.
  • Compared eye tracking results against manual annotations of data relevance.

Main Results:

  • Eye tracking achieved an accuracy of 69% and a precision of 53% when compared to manual annotation.
  • The developed gaze point processing methods and scripts represent a foundational step.

Conclusions:

  • Eye tracking offers a promising, novel application for collecting training data for machine learning in clinical decision support.
  • Further development of eye tracking methodologies can advance the creation of intelligent EMR-integrated tools.