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
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/naacl24-info/D18-1304.pdf