2017
EMNLP
EMNLP 2017
An analysis of eye-movements during reading for the detection of mild cognitive impairment
Abstract
AbstractWe present a machine learning analysis of eye-tracking data for the detection of mild cognitive impairment, a decline in cognitive abilities that is associated with an increased risk of developing dementia. We compare two experimental configurations (reading aloud versus reading silently), as well as two methods of combining information from the two trials (concatenation and merging). Additionally, we annotate the words being read with information about their frequency and syntactic category, and use these annotations to generate new features. Ultimately, we are able to distinguish between participants with and without cognitive impairment with up to 86% accuracy.
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Interdisciplinary Bridge
— Data Science & Analytics and Machine Learning
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Trend Setter
— Disease Surveillance
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Keyword Pioneer
— reading behavior
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Hot Topic Early Bird
— binary classification
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio