@inproceedings{madeira-etal-2020-framework,
title = "A Framework to Assist Chat Operators of Mental Healthcare Services",
author = "Madeira, Thiago and
Bernardino, Heder and
Francisco De Souza, Jairo and
Gomide, Henrique and
Munck Machado, Nath{\'a}lia and
Marcos Pinheiro da Silva, Bruno and
Vieira Pereira Pacelli, Alexandre",
editor = "Park, Eunjeong L. and
Hagiwara, Masato and
Milajevs, Dmitrijs and
Liu, Nelson F. and
Chauhan, Geeticka and
Tan, Liling",
booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nlposs-1.1",
doi = "10.18653/v1/2020.nlposs-1.1",
pages = "1--7",
abstract = "Conversational agents can be used to make diagnoses, classify mental states, promote health education, and provide emotional support. The benefits of adopting conversational agents include widespread access, increased treatment engagement, and improved patient relationships with the intervention. We propose here a framework to assist chat operators of mental healthcare services, instead of a fully automated conversational agent. This design eases to avoid the adverse effects of applying chatbots in mental healthcare. The proposed framework is capable of improving the quality and reducing the time of interactions via chat between a user and a chat operator. We also present a case study in the context of health promotion on reducing tobacco use. The proposed framework uses artificial intelligence, specifically natural language processing (NLP) techniques, to classify messages from chat users. A list of suggestions is offered to the chat operator, with topics to be discussed in the session. These suggestions were created based on service protocols and the classification of previous chat sessions. The operator can also edit the suggested messages. Data collected can be used in the future to improve the quality of the suggestions offered.",
}
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<abstract>Conversational agents can be used to make diagnoses, classify mental states, promote health education, and provide emotional support. The benefits of adopting conversational agents include widespread access, increased treatment engagement, and improved patient relationships with the intervention. We propose here a framework to assist chat operators of mental healthcare services, instead of a fully automated conversational agent. This design eases to avoid the adverse effects of applying chatbots in mental healthcare. The proposed framework is capable of improving the quality and reducing the time of interactions via chat between a user and a chat operator. We also present a case study in the context of health promotion on reducing tobacco use. The proposed framework uses artificial intelligence, specifically natural language processing (NLP) techniques, to classify messages from chat users. A list of suggestions is offered to the chat operator, with topics to be discussed in the session. These suggestions were created based on service protocols and the classification of previous chat sessions. The operator can also edit the suggested messages. Data collected can be used in the future to improve the quality of the suggestions offered.</abstract>
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%0 Conference Proceedings
%T A Framework to Assist Chat Operators of Mental Healthcare Services
%A Madeira, Thiago
%A Bernardino, Heder
%A Francisco De Souza, Jairo
%A Gomide, Henrique
%A Munck Machado, Nathália
%A Marcos Pinheiro da Silva, Bruno
%A Vieira Pereira Pacelli, Alexandre
%Y Park, Eunjeong L.
%Y Hagiwara, Masato
%Y Milajevs, Dmitrijs
%Y Liu, Nelson F.
%Y Chauhan, Geeticka
%Y Tan, Liling
%S Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F madeira-etal-2020-framework
%X Conversational agents can be used to make diagnoses, classify mental states, promote health education, and provide emotional support. The benefits of adopting conversational agents include widespread access, increased treatment engagement, and improved patient relationships with the intervention. We propose here a framework to assist chat operators of mental healthcare services, instead of a fully automated conversational agent. This design eases to avoid the adverse effects of applying chatbots in mental healthcare. The proposed framework is capable of improving the quality and reducing the time of interactions via chat between a user and a chat operator. We also present a case study in the context of health promotion on reducing tobacco use. The proposed framework uses artificial intelligence, specifically natural language processing (NLP) techniques, to classify messages from chat users. A list of suggestions is offered to the chat operator, with topics to be discussed in the session. These suggestions were created based on service protocols and the classification of previous chat sessions. The operator can also edit the suggested messages. Data collected can be used in the future to improve the quality of the suggestions offered.
%R 10.18653/v1/2020.nlposs-1.1
%U https://aclanthology.org/2020.nlposs-1.1
%U https://doi.org/10.18653/v1/2020.nlposs-1.1
%P 1-7
Markdown (Informal)
[A Framework to Assist Chat Operators of Mental Healthcare Services](https://aclanthology.org/2020.nlposs-1.1) (Madeira et al., NLPOSS 2020)
ACL
- Thiago Madeira, Heder Bernardino, Jairo Francisco De Souza, Henrique Gomide, Nathália Munck Machado, Bruno Marcos Pinheiro da Silva, and Alexandre Vieira Pereira Pacelli. 2020. A Framework to Assist Chat Operators of Mental Healthcare Services. In Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS), pages 1–7, Online. Association for Computational Linguistics.