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Evaluation of eye tracking for a decision support application.


ABSTRACT: Eye tracking is used widely to investigate attention and cognitive processes while performing tasks in electronic medical record (EMR) systems. We explored a novel application of eye tracking to collect training data for a machine learning-based clinical decision support tool that predicts which patient data are likely to be relevant for a clinical task. Specifically, we investigated in a laboratory setting the accuracy of eye tracking compared to manual annotation for inferring which patient data in the EMR are judged to be relevant by physicians. We evaluated several methods for processing gaze points that were recorded using a low-cost eye-tracking device. Our results show that eye tracking achieves accuracy and precision of 69% and 53%, respectively compared to manual annotation and are promising for machine learning. The methods for processing gaze points and scripts that we developed offer a first step in developing novel uses for eye tracking for clinical decision support.

SUBMITTER: Visweswaran S 

PROVIDER: S-EPMC8327376 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Evaluation of eye tracking for a decision support application.

Visweswaran Shyam S   King Andrew J AJ   Tajgardoon Mohammadamin M   Calzoni Luca L   Clermont Gilles G   Hochheiser Harry H   Cooper Gregory F GF  

JAMIA open 20210701 3


Eye tracking is used widely to investigate attention and cognitive processes while performing tasks in electronic medical record (EMR) systems. We explored a novel application of eye tracking to collect training data for a machine learning-based clinical decision support tool that predicts which patient data are likely to be relevant for a clinical task. Specifically, we investigated in a laboratory setting the accuracy of eye tracking compared to manual annotation for inferring which patient da  ...[more]

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