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Structured Handwritten Input for Dementia Classification

Author(s)
Flores, Gerardo
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Advisor
Davis, Randall
Terms of use
In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/
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Abstract
We explore the use of deep learning to score the Digit Symbol Substitution Test (DSST), a paper-and-pencil behavioral test useful in diagnosing Alzheimer’s. We train a model to classify Alzheimer’s based on the subject’s responses to any one of the 108 queries in the test. We then combine predictions across the test to produce a new classifier that is considerably stronger. We also make an extensive search of architectures and optimization techniques that have proved useful in other settings. The ultimate result is a very strong classifier, with AUC for a response to a single question of 86% and for an overall patient of 97.25%.
Date issued
2024-09
URI
https://hdl.handle.net/1721.1/158498
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology

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