Learning multiview embeddings for assessing dementia

Chloé Pou-Prom, Frank Rudzicz


Abstract
As the incidence of Alzheimer’s Disease (AD) increases, early detection becomes crucial. Unfortunately, datasets for AD assessment are often sparse and incomplete. In this work, we leverage the multiview nature of a small AD dataset, DementiaBank, to learn an embedding that captures different modes of cognitive impairment. We apply generalized canonical correlation analysis (GCCA) to our dataset and demonstrate the added benefit of using multiview embeddings in two downstream tasks: identifying AD and predicting clinical scores. By including multiview embeddings, we obtain an F1 score of 0.82 in the classification task and a mean absolute error of 3.42 in the regression task. Furthermore, we show that multiview embeddings can be obtained from other datasets as well.
Anthology ID:
D18-1304
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2812–2817
Language:
URL:
https://aclanthology.org/D18-1304
DOI:
10.18653/v1/D18-1304
Bibkey:
Cite (ACL):
Chloé Pou-Prom and Frank Rudzicz. 2018. Learning multiview embeddings for assessing dementia. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2812–2817, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Learning multiview embeddings for assessing dementia (Pou-Prom & Rudzicz, EMNLP 2018)
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PDF:
https://preview.aclanthology.org/update-css-js/D18-1304.pdf
Video:
 https://vimeo.com/305892345