Abstract
The use of spontaneous language to derive appropriate digital markers has become an emergent, promising and non-intrusive method to diagnose and monitor dementia. Here we propose methods to capture language coherence as a cost-effective, human-interpretable digital marker for monitoring cognitive changes in people with dementia. We introduce a novel task to learn the temporal logical consistency of utterances in short transcribed narratives and investigate a range of neural approaches. We compare such language coherence patterns between people with dementia and healthy controls and conduct a longitudinal evaluation against three clinical bio-markers to investigate the reliability of our proposed digital coherence marker. The coherence marker shows a significant difference between people with mild cognitive impairment, those with Alzheimer’s Disease and healthy controls. Moreover our analysis shows high association between the coherence marker and the clinical bio-markers as well as generalisability potential to other related conditions.- Anthology ID:
- 2023.emnlp-main.994
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 16021–16034
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2023.emnlp-main.994/
- DOI:
- 10.18653/v1/2023.emnlp-main.994
- Cite (ACL):
- Dimitris Gkoumas, Adam Tsakalidis, and Maria Liakata. 2023. A Digital Language Coherence Marker for Monitoring Dementia. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 16021–16034, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- A Digital Language Coherence Marker for Monitoring Dementia (Gkoumas et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2023.emnlp-main.994.pdf