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 exensive 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%.
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