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Teles, A.; , .; Fontenele, J.; Oliveira, A.; Bastos, V.H.; Teixeira, S.; Rabelo, R.; Dos Santos, R. Boamente. Encyclopedia. Available online: https://encyclopedia.pub/entry/21801 (accessed on 25 April 2024).
Teles A,  , Fontenele J, Oliveira A, Bastos VH, Teixeira S, et al. Boamente. Encyclopedia. Available at: https://encyclopedia.pub/entry/21801. Accessed April 25, 2024.
Teles, Ariel, , José Fontenele, Adonias Oliveira, Victor Hugo Bastos, Silmar Teixeira, Ricardo Rabelo, Renato Dos Santos. "Boamente" Encyclopedia, https://encyclopedia.pub/entry/21801 (accessed April 25, 2024).
Teles, A., , ., Fontenele, J., Oliveira, A., Bastos, V.H., Teixeira, S., Rabelo, R., & Dos Santos, R. (2022, April 15). Boamente. In Encyclopedia. https://encyclopedia.pub/entry/21801
Teles, Ariel, et al. "Boamente." Encyclopedia. Web. 15 April, 2022.
Boamente
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This entry is adapted from the peer-reviewed paper 10.3390/healthcare10040698

People 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 entry aimed to develop the Boamente tool, a solution that collects textual data from users’ smartphones and identifies the existence of suicidal ideation.

artificial intelligence deep learning eHealth mental health

1. Background

Suicide is one of the main causes of death in the world [1]. In 2019, Brazil was among ten countries where the most suicides occurred in the world, and the second among countries of the Americas, with 14,540 suicide cases [2]. According to the World Health Organization (WHO), 703,000 people committed suicide in 2019 in the world. As an aggravating factor, the current COVID-19 pandemic has changed people’s well-being and mental health due to different events, such as deaths, social isolation, and job closures, which can also cause an increase in the number of people at risk of suicide [3][4].
Several factors can influence individuals to make the decision to end their lives (for example, emotional pain, marital problems, and biological, genetic, psychological, social, cultural, financial, and environmental factors) [5][6][7]. According to the WHO, when people are mentally healthy, they are able to be productive, contribute to the community, and recover from the stress they experience daily [1]. In contrast, mental disorders can negatively impact people’s lives, in addition to affecting relationships with friends, family, and health systems. Anyone can have suicidal ideation [8].
To prevent suicide, there has been rapid growth in the development and use of digital technologies [9], such as mobile applications [10][11], which can identify, monitor, and support individuals at risk. In particular, mobile applications for digital phenotyping aim at collecting information to objectively contribute to the identification of symptoms and behaviors of interest to mental health professionals (for example, psychologists and psychiatrists) [12][13]. According to Torous et al. [14], the term “digital phenotyping” refers to a “moment-by-moment quantification of the individual-level human phenotype in-situ using data from smartphones and other personal digital devices”. Digital phenotyping mobile applications use people’s interactions with smartphone applications in everyday environments to facilitate remote monitoring of their behaviors and habits, requiring little or no direct interaction for data collection.
Usually, people at risk of suicide tend to be isolated and cannot share their suicidal thoughts with their family, friends, or even mental health professionals [15]. At the same time, people may express their emotions, thoughts, and feelings in a variety of ways, including through text messages on social media (for example, Twitter, Facebook, Instagram, and Reddit) [16]. These texts, obtained from online social media, may be defined as non-clinical texts [17], as they are not annotated by health professionals. Non-clinical texts can be obtained from different sources, but social media can produce large quantities available at any time. Such a characteristic (the high availability at any time) enables non-clinical texts to be explored in studies that use machine/deep learning (ML/DL) and natural language processing (NLP) techniques to identify suicidal ideation [18]. Such techniques have demonstrated their potential to perform different tasks in the healthcare field [19][20].

2. Related Work

Suicide is an intriguing form of human death, and its motivations are complex [21]. Therefore, the timely identification of an individual at risk of suicide is a hard task. For this reason, different studies have taken advantage of information and communication technologies (ICT), such as ML/DL techniques [22][23][24] and mobile applications [10][25][26], to identify suicidal patterns and behaviors. Such studies seek to propose computer solutions enabled for the early identification of people at risk of suicide. Thus, solutions are proposed to prevent suicide from happening.
Most mobile applications for suicide prevention provide features for ecological momentary assessment (EMA) [27] and ecological momentary intervention (EMI) [28], such as emma [29], and coping tools, such as CALMA [30]. There are a few digital phenotyping applications for suicide prevention and, specifically, solutions focused on detecting suicidal ideation. Strength Within Me [31][32] is a digital phenotyping mobile application developed to sense data that are useful for predicting suicidality. It collects contextual information, usually gathered from smartphone sensors, such as sleep behavior, mood, and steps, to be correlated with user answers obtained from a suicide severity rating scale. The collected data are used as inputs to test ML models for predicting suicide risk. Studies using this digital phenotyping application [31][32] demonstrated its feasibility to detect risk of suicidality.
Another proposed digital phenotyping mobile application for monitoring suicidal ideation is SIMON [33]. This solution is composed of two parts: SIMON-SELF, which is an EMA application that uses a conversational agent (a chatbot) to request self-reports from users; and SIMON-SENSE, a sensing application used to passively collect contextual data from the user’s smartphone (for example, data produced by an accelerometer, GPS, Bluetooth, Wi-Fi) and identify situations of interest (for example, physical activity, location, and social connectedness). Collected data will be used as inputs to develop ML models for predicting suicidal ideation and psychiatric hospital re-admission.
Studies focusing on developing ML/DL models may use non-clinical texts to identify harmful content related to suicide. Burnap et al. [34] used non-clinical texts related to suicide to train several ML algorithms. This study aimed at classifying texts relating to suicide on Twitter. The study motivation is based on the fact that suicide-related posts can represent a risk to the users of online social networks, who could encourage them to hurt themselves. Classifiers were trained to distinguish between suicidal ideation and other suicide-related content (for example, suicide reports, memorials, campaigning, and support).
Psychiatric stressors (see [35] for definition) related to suicide were detected by Du et al. in [36]. For this purpose, the authors used user posts (non-clinical texts) obtained from Twitter and a convolutional neural network (CNN) to classify them into positive (that is, related to suicide or suicide ideation) and negative (that is, unrelated to suicide or suicide ideation) classes. Next, psychiatric stressors were annotated in the tweets labeled as positive, and a recurrent neural network (RNN) was used to extract stressors from positive tweets. Models created using different ML/DL algorithms were compared to identify the best one. This study achieved promising results in the process of identifying psychiatric stressors.
In the work by Ophir et al. [37], two deep neural network models using the Facebook posts of users to predict suicide risk were developed. The first model was able to predict suicide risk from posts. The second one was focused on predicting a hierarchical combination of multiple factors (for example, personality traits, psychosocial risks, and psychiatric disorders) to mediate the link between Facebook posts and suicide risk.
Most of the works have developed models using the English language as input. Carvalho et al. [38] started the study for suicidal ideation detection using texts written in Brazilian Portuguese (PT-BR). This work used texts obtained from Twitter to develop and compare three different ML/DL models. Posts were labeled using two approaches: three classes (safe to ignore, possibly worrying, and strongly worrying) and two classes (safe to ignore and possibly worrying). Results demonstrated that the bidirectional encoder representations from transformers (BERT) model [39] obtained the best performance in the two approaches; researchers have considered the models developed in Carvalho et al.’s study to compare with model.

References

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  2. World Health Organization. Suicide Worldwide in 2019: Global Health Estimates. Available online: https://www.who.int/publications/i/item/9789240026643 (accessed on 14 January 2022).
  3. Menon, V.; Padhy, S.K.; Pattnaik, J.I. COVID-19 pandemic and suicidality: Durkheim revisited. Aust. N. Z. J. Psychiatry 2021, 55, 324.
  4. Lin, C.Y.; Alimoradi, Z.; Ehsani, N.; Ohayon, M.M.; Chen, S.H.; Griffiths, M.D.; Pakpour, A.H. Suicidal Ideation during the COVID-19 Pandemic among A Large-Scale Iranian Sample: The Roles of Generalized Trust, Insomnia, and Fear of COVID-19. Healthcare 2022, 10, 93.
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  7. van Heeringen, K.; Mann, J.J. The neurobiology of suicide. Lancet Psychiatry 2014, 1, 63–72.
  8. Harmer, B.; Lee, S.; Saadabadi, A. Suicidal ideation. Statpearls 2021. Available online: https://www.ncbi.nlm.nih.gov/books/NBK565877/ (accessed on 31 January 2022).
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  11. Teles, A.; Rodrigues, I.; Viana, D.; Silva, F.; Coutinho, L.; Endler, M.; Rabêlo, R. Mobile Mental Health: A Review of Applications for Depression Assistance. In Proceedings of the 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), Cordoba, Spain, 5–7 June 2019; pp. 708–713.
  12. Liang, Y.; Xiaolong, Z.; Zeng, D.D. A Survey on Big Data-Driven Digital Phenotyping of Mental Health. Inf. Fusion 2019, 52, 290–307.
  13. Mendes, J.P.M.; Moura, I.R.; Van de Ven, P.; Viana, D.; Silva, F.J.S.; Coutinho, L.R.; Teixeira, S.; Rodrigues, J.J.P.C.; Teles, A.S. Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review. J. Med. Internet Res. 2022, 24, e28735.
  14. Torous, J.; Kiang, M.V.; Lorme, J.; Onnela, J.P. New Tools for New Research in Psychiatry: A Scalable and Customizable Platform to Empower Data Driven Smartphone Research. Jmir Ment. Health 2016, 3, e16.
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  16. Tadesse, M.M.; Lin, H.; Xu, B.; Yang, L. Detection of Depression-Related Posts in Reddit Social Media Forum. IEEE Access 2019, 7, 44883–44893.
  17. Calvo, R.; Milne, D.; Hussain, S.; Christensen, H. Natural language processing in mental health applications using non-clinical texts. Nat. Lang. Eng. 2017, 1–37.
  18. Wongkoblap, A.; Vadillo, M.A.; Curcin, V. Researching Mental Health Disorders in the Era of Social Media: Systematic Review. J. Med. Internet Res. 2017, 19, e228.
  19. Ji, S.; Zhang, T.; Ansari, L.; Fu, J.; Tiwari, P.; Cambria, E. MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare. arXiv 2021, arXiv:2110.15621.
  20. Elbattah, M.; Arnaud, É.; Gignon, M.; Dequen, G. The Role of Text Analytics in Healthcare: A Review of Recent Developments and Applications. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, Online, 11–13 February 2021; pp. 825–832.
  21. O’Connor, R.C.; Kirtley, O.J. The integrated motivational - volitional model of suicidal behaviour. Philos. Trans. R. Soc. Biol. Sci. 2018, 373, 20170268.
  22. Ji, S.; Pan, S.; Li, X.; Cambria, E.; Long, G.; Huang, Z. Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications. IEEE Trans. Comput. Soc. Syst. 2021, 8, 214–226.
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  24. Castillo-Sánchez, G.; Marques, G.; Dorronzoro, E.; Rivera-Romero, O.; Franco-Martín, M.; De la Torre-Díez, I. Suicide Risk Assessment Using Machine Learning and Social Networks: A Scoping Review. J. Med. Syst. 2020, 44, 1–15.
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  29. Morgiève, M.; Genty, C.; Azé, J.; Dubois, J.; Leboyer, M.; Vaiva, G.; Berrouiguet, S.; Courtet, P. A Digital Companion, the Emma App, for Ecological Momentary Assessment and Prevention of Suicide: Quantitative Case Series Study. JMIR Mhealth Uhealth 2020, 8, e15741.
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  31. Haines-Delmont, A.; Chahal, G.; Bruen, A.J.; Wall, A.; Khan, C.T.; Sadashiv, R.; Fearnley, D. Testing Suicide Risk Prediction Algorithms Using Phone Measurements With Patients in Acute Mental Health Settings: Feasibility Study. JMIR Mhealth Uhealth 2020, 8, e15901.
  32. Bruen, A.J.; Wall, A.; Haines-Delmont, A.; Perkins, E. Exploring Suicidal Ideation Using an Innovative Mobile App-Strength Within Me: The Usability and Acceptability of Setting up a Trial Involving Mobile Technology and Mental Health Service Users. JMIR Mental Health 2020, 7, e18407.
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  34. Burnap, P.; Colombo, G.; Amery, R.; Hodorog, A.; Scourfield, J. Multi-class machine classification of suicide-related communication on Twitter. Online Soc. Netw. Media 2017, 2, 32–44.
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  36. Du, J.; Zhang, Y.; Luo, J.; Jia, Y.; Wei, Q.; Tao, C.; Xu, H. Extracting psychiatric stressors for suicide from social media using deep learning. Bmc Med. Informatics Decis. Mak. 2018, 18, 43.
  37. Ophir, Y.; Tikochinski, R.; Asterhan, C.S.; Sisso, I.; Reichart, R. Deep neural networks detect suicide risk from textual facebook posts. Sci. Rep. 2020, 10, 1–10.
  38. de Carvalho, V.F.; Giacon, B.; Nascimento, C.; Nogueira, B.M. Machine Learning for Suicidal Ideation Identification on Twitter for the Portuguese Language. In Brazilian Conference on Intelligent Systems; Springer: Berlin/Heidelberg, Germany, 2020; pp. 536–550.
  39. Devlin, J.; Chang, M.; Lee, K.; Toutanova, K. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT); Burstein, J., Doran, C., Solorio, T., Eds.; Association for Computational Linguistics: Stroudsburg, PA, USA, 2019; pp. 4171–4186.
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Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Ariel Teles
,
,
José Fontenele
,
Adonias OLIVEIRA
,
Victor Hugo Bastos
,
Silmar Teixeira
,
Ricardo Rabelo
,
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Update Date: 15 Apr 2022
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