Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-57351-9_29 | ADVANCES IN ARTIFICIAL INTELLIGENCE, CANADIAN AI 2017 |
Keywords | Field | DocType |
Domain adaptation,Mild cognitive impairment,Dementia,Alzheimer's | Computer science,Domain adaptation,Cognitive psychology,Artificial intelligence,Augment,Machine learning,Spoken language,Cognitive impairment,Dementia | Conference |
Volume | ISSN | Citations |
10233 | 0302-9743 | 1 |
PageRank | References | Authors |
0.41 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vaden Masrani | 1 | 2 | 3.12 |
| Gabriel Murray | 2 | 164 | 17.30 |
| Thalia Shoshana Field | 3 | 1 | 0.75 |
| Giuseppe Carenini | 4 | 1461 | 111.12 |