@inproceedings{pou-prom-rudzicz-2018-learning,
title = "Learning multiview embeddings for assessing dementia",
author = "Pou-Prom, Chlo{\'e} and
Rudzicz, Frank",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1304",
doi = "10.18653/v1/D18-1304",
pages = "2812--2817",
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.",
}
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%0 Conference Proceedings
%T Learning multiview embeddings for assessing dementia
%A Pou-Prom, Chloé
%A Rudzicz, Frank
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F pou-prom-rudzicz-2018-learning
%X 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.
%R 10.18653/v1/D18-1304
%U https://aclanthology.org/D18-1304
%U https://doi.org/10.18653/v1/D18-1304
%P 2812-2817
Markdown (Informal)
[Learning multiview embeddings for assessing dementia](https://aclanthology.org/D18-1304) (Pou-Prom & Rudzicz, EMNLP 2018)
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.