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Automatic detection of cognitive impairment in elderly people using an entertainment chatbot with Natural Language Processing capabilities

Previous researchers have proposed intelligent systems for therapeutic monitoring of cognitive impairments. However, most existing practical approaches for this purpose are based on manual tests. This raises issues such as excessive caretaking effort and the white-coat effect. To avoid these issues,...

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Autores principales: de Arriba-Pérez, Francisco, García-Méndez, Silvia, González-Castaño, Francisco J., Costa-Montenegro, Enrique
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9053565/
https://www.ncbi.nlm.nih.gov/pubmed/35529905
http://dx.doi.org/10.1007/s12652-022-03849-2
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author de Arriba-Pérez, Francisco
García-Méndez, Silvia
González-Castaño, Francisco J.
Costa-Montenegro, Enrique
author_facet de Arriba-Pérez, Francisco
García-Méndez, Silvia
González-Castaño, Francisco J.
Costa-Montenegro, Enrique
author_sort de Arriba-Pérez, Francisco
collection PubMed
description Previous researchers have proposed intelligent systems for therapeutic monitoring of cognitive impairments. However, most existing practical approaches for this purpose are based on manual tests. This raises issues such as excessive caretaking effort and the white-coat effect. To avoid these issues, we present an intelligent conversational system for entertaining elderly people with news of their interest that monitors cognitive impairment transparently. Automatic chatbot dialogue stages allow assessing content description skills and detecting cognitive impairment with Machine Learning algorithms. We create these dialogue flows automatically from updated news items using Natural Language Generation techniques. The system also infers the gold standard of the answers to the questions, so it can assess cognitive capabilities automatically by comparing these answers with the user responses. It employs a similarity metric with values in [0, 1], in increasing level of similarity. To evaluate the performance and usability of our approach, we have conducted field tests with a test group of 30 elderly people in the earliest stages of dementia, under the supervision of gerontologists. In the experiments, we have analysed the effect of stress and concentration in these users. Those without cognitive impairment performed up to five times better. In particular, the similarity metric varied between 0.03, for stressed and unfocused participants, and 0.36, for relaxed and focused users. Finally, we developed a Machine Learning algorithm based on textual analysis features for automatic cognitive impairment detection, which attained accuracy, F-measure and recall levels above 80%. We have thus validated the automatic approach to detect cognitive impairment in elderly people based on entertainment content. The results suggest that the solution has strong potential for long-term user-friendly therapeutic monitoring of elderly people.
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spelling pubmed-90535652022-05-02 Automatic detection of cognitive impairment in elderly people using an entertainment chatbot with Natural Language Processing capabilities de Arriba-Pérez, Francisco García-Méndez, Silvia González-Castaño, Francisco J. Costa-Montenegro, Enrique J Ambient Intell Humaniz Comput Original Research Previous researchers have proposed intelligent systems for therapeutic monitoring of cognitive impairments. However, most existing practical approaches for this purpose are based on manual tests. This raises issues such as excessive caretaking effort and the white-coat effect. To avoid these issues, we present an intelligent conversational system for entertaining elderly people with news of their interest that monitors cognitive impairment transparently. Automatic chatbot dialogue stages allow assessing content description skills and detecting cognitive impairment with Machine Learning algorithms. We create these dialogue flows automatically from updated news items using Natural Language Generation techniques. The system also infers the gold standard of the answers to the questions, so it can assess cognitive capabilities automatically by comparing these answers with the user responses. It employs a similarity metric with values in [0, 1], in increasing level of similarity. To evaluate the performance and usability of our approach, we have conducted field tests with a test group of 30 elderly people in the earliest stages of dementia, under the supervision of gerontologists. In the experiments, we have analysed the effect of stress and concentration in these users. Those without cognitive impairment performed up to five times better. In particular, the similarity metric varied between 0.03, for stressed and unfocused participants, and 0.36, for relaxed and focused users. Finally, we developed a Machine Learning algorithm based on textual analysis features for automatic cognitive impairment detection, which attained accuracy, F-measure and recall levels above 80%. We have thus validated the automatic approach to detect cognitive impairment in elderly people based on entertainment content. The results suggest that the solution has strong potential for long-term user-friendly therapeutic monitoring of elderly people. Springer Berlin Heidelberg 2022-04-29 /pmc/articles/PMC9053565/ /pubmed/35529905 http://dx.doi.org/10.1007/s12652-022-03849-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visithttp://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Research
de Arriba-Pérez, Francisco
García-Méndez, Silvia
González-Castaño, Francisco J.
Costa-Montenegro, Enrique
Automatic detection of cognitive impairment in elderly people using an entertainment chatbot with Natural Language Processing capabilities
title Automatic detection of cognitive impairment in elderly people using an entertainment chatbot with Natural Language Processing capabilities
title_full Automatic detection of cognitive impairment in elderly people using an entertainment chatbot with Natural Language Processing capabilities
title_fullStr Automatic detection of cognitive impairment in elderly people using an entertainment chatbot with Natural Language Processing capabilities
title_full_unstemmed Automatic detection of cognitive impairment in elderly people using an entertainment chatbot with Natural Language Processing capabilities
title_short Automatic detection of cognitive impairment in elderly people using an entertainment chatbot with Natural Language Processing capabilities
title_sort automatic detection of cognitive impairment in elderly people using an entertainment chatbot with natural language processing capabilities
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9053565/
https://www.ncbi.nlm.nih.gov/pubmed/35529905
http://dx.doi.org/10.1007/s12652-022-03849-2
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