Abstract
Lexical and acoustic markers in spoken language can be used to detect mild cognitive impairment (MCI), a condition which is often a precursor to dementia and frequently causes some degree of dysphasia. Research to develop such a diagnostic tool for clinicians has been hindered by the scarcity of available data. This work uses domain adaptation to adapt Alzheimer’s data to improve classification accuracy of MCI. We evaluate two simple domain adaptation algorithms, AUGMENT and CORAL, and show that AUGMENT improves upon all baselines. Additionally we investigate the use of previously unconsidered discourse features and show they are not useful in distinguishing MCI from healthy controls.
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Notes
- 1.
- 2.
We used ZCA whitening which is discussed in greater detail here: http://ufldl.stanford.edu/wiki/index.php/Whitening.
- 3.
Available at: http://nlp.stanford.edu/software/tagger.shtml.
- 4.
With one small modification: We ran a 7-fold cross validation instead of 10-fold because there was not enough target data in the 25% trial to divide into 10 folds.
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Masrani, V., Murray, G., Field, T.S., Carenini, G. (2017). Domain Adaptation for Detecting Mild Cognitive Impairment. In: Mouhoub, M., Langlais, P. (eds) Advances in Artificial Intelligence. Canadian AI 2017. Lecture Notes in Computer Science(), vol 10233. Springer, Cham. https://doi.org/10.1007/978-3-319-57351-9_29
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