A Chat Recommender System for COVID-19 Support based in Textual Sentence Embeddings
2021 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2021
; : 248-252, 2021.
Article
in English
| Scopus | ID: covidwho-1832584
ABSTRACT
With the emergence of the COVID-19 pandemic, the demand for health services has exponentially increased, which caused the saturation of hospital beds and a high death toll. Motivated by the need to provide more agility in patients' attendance and unburden the health services, this work proposes a solution for automatic attendance via a Recommender System that uses sentence embeddings of text messages to train an LSTM classifier. This classifier can provide recommendations of a course of action for patients, instructing them to stay at home or seek medical support. Our numerical results validate the proposed solution and corroborate its reasonable accuracy rate. © 2021 ACM.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
WIC
Year:
2021
Document Type:
Article
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