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    <titleInfo>
        <title>A Digital Language Coherence Marker for Monitoring Dementia</title>
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        <namePart type="family">Liakata</namePart>
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            <namePart type="given">Houda</namePart>
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            <namePart type="given">Juan</namePart>
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        <name type="personal">
            <namePart type="given">Kalika</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.994</identifier>
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%0 Conference Proceedings
%T A Digital Language Coherence Marker for Monitoring Dementia
%A Gkoumas, Dimitris
%A Tsakalidis, Adam
%A Liakata, Maria
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F gkoumas-etal-2023-digital
%X 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.
%R 10.18653/v1/2023.emnlp-main.994
%U https://aclanthology.org/2023.emnlp-main.994
%U https://doi.org/10.18653/v1/2023.emnlp-main.994
%P 16021-16034

Markdown (Informal)

[A Digital Language Coherence Marker for Monitoring Dementia](https://aclanthology.org/2023.emnlp-main.994) (Gkoumas et al., EMNLP 2023)

• A Digital Language Coherence Marker for Monitoring Dementia (Gkoumas et al., EMNLP 2023)

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.