Abstract
The proposed approach can filter, study, analyze, and interpret written communications from social media platforms for early detection of Cognitive Impairment (CI) to connect individuals with CI with assistive services in their location. It has three novel functionalities. First, it presents a Big Data-centric Data Mining methodology that uses a host of Natural Language Processing and Information Retrieval approaches to filter and analyze tweets to detect if the tweets were made by a user with some form of CI – for instance, Dementia. Second, it consists of a string-matching functionality that uses the Levenshtein distance algorithm and Fuzzy matching to score tweets indicating the degree of CI. Finally, the framework consists of a text mining approach for detecting the geolocation of the Twitter user so that, if the user is cognitively impaired, caregivers in that area could be alerted and connected to them to facilitate early-stage care, services, therapies, or treatment.
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Thakur, N., Han, C.Y. (2022). A Multimodal Approach for Early Detection of Cognitive Impairment from Tweets. In: Ahram, T., Taiar, R. (eds) Human Interaction, Emerging Technologies and Future Systems V. IHIET 2021. Lecture Notes in Networks and Systems, vol 319. Springer, Cham. https://doi.org/10.1007/978-3-030-85540-6_2
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