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Esta atividade de avaliação do protótipo faz parte de um projeto de pesquisa de doutorado do Programa de Pós-graduação em Biotecnologia em Saúde (PPGBiotec) da Universidade Federal do Delta do Parnaíba (UFDPar).

Os seguintes artigos relacionados com esta pesquisa foram publicados:

Diniz, Evandro J. S., José E. Fontenele, Adonias C. de Oliveira, Victor H. Bastos, Silmar Teixeira, Ricardo L. Rabêlo, Dario B. Calçada, Renato M. dos Santos, Ana K. de Oliveira, and Ariel S. Teles. 2022. "Boamente: A Natural Language Processing-Based Digital Phenotyping Tool for Smart Monitoring of Suicidal Ideation" Healthcare 10, no. 4: 698. https://doi.org/10.3390/healthcare10040698

Boamente: A Natural Language Processing-Based Digital Phenotyping Tool for Smart Monitoring of Suicidal IdeationPeople at risk of suicide tend to be isolated and cannot share their thoughts. For this reason, suicidal ideation monitoring becomes a hard task. Therefore, people at risk of suicide need to be monitored in a manner capable of identifying if and when they have a suicidal ideation, enabling professionals to perform timely interventions. This study aimed to develop the Boamente tool, a solution that collects textual data from users’ smartphones and identifies the existence of suicidal ideation. The solution has a virtual keyboard mobile application that passively collects user texts and sends them to a web platform to be processed. The platform classifies texts using natural language processing and a deep learning model to recognize suicidal ideation, and the results are presented to mental health professionals in dashboards. Text classification for sentiment analysis was implemented with different machine/deep learning algorithms. A validation study was conducted to identify the model with the best performance results. The BERTimbau Large model performed better, reaching a recall of 0.953 (accuracy: 0.955; precision: 0.961; F-score: 0.954; AUC: 0.954). The proposed tool demonstrated an ability to identify suicidal ideation from user texts, which enabled it to be experimented with in studies with professionals and their patients.

Adonias C. de Oliveira, Evandro J.S. Diniz, Silmar Teixeira, Ariel S. Teles. How can machine learning identify suicidal ideation from user's . texts? Towards the explanation of the Boamente system, Procedia Computer Science, Volume 206, 2022, Pages 141-150, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2022.09.093.

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