Thoth: An intelligent model for assisting individuals with suicidal ideation
Resumo
Suicide causes approximately 800,000 deaths worldwide every year, which means one death by suicide every 40 seconds. Suicidal ideation is the first stage in the suicide risk scale, in which the individuals have thoughts regarding being dead. Thereby, suicide prevention strategies may focus on identifying and treating individuals with this severity level. Therefore, this article presents the summary of an Academic Master’s Dissertation that proposes Thoth, a computational model for assisting people suffering from suicidal ideation. The main scientific contribution of the Thoth is the personalized assistance for individuals at risk of suicidal ideation through the analysis of Context Information for anticipating the identification of future risks. The model gathers sensor, sociodemographic, and psychological data for future risk checking through Machine Learning models. Experiments showed that the models obtained F1-Score up to 94.12%. Based on the experiments, Thoth could act in a personalized manner, sending recommendations and alerts to patients and caregivers, respectively. Thus, this research provides an improvement in the assistance of individuals with suicidal ideation through the proposed model.
Palavras-chave:
Context Information, Machine Learning, Mental Health, Patient Assistance, Suicidal Ideation, Suicide Prevention
Referências
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Jorge Arthur Schneider Aranda, Rodrigo Simon Bavaresco, Juliano Varella de Carvalho, Adenauer Corrêa Yamin, Mauricio Campelo Tavares, and Jorge Luis Victória Barbosa. 2021. A computational model for adaptive recording of vital signs through context histories. Journal of Ambient Intelligence and Humanized Computing (2021), 1–15. https://doi.org/10.1007/s12652-021-03126-8
Fred D. Davis. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems 13, 3 (1989), 319–339. https://doi.org/10.2307/249008
Anind Dey, Gregory Abowd, and Daniel Salber. 2001. A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of Context-Aware Applications. Human-Computer Interaction 16 (2001), 97–166.
Chris Evans, Janice Connell, Michael Barkham, Frank Margison, Graeme McGrath, John Mellor-Clark, and Kerry Audin. 2002. Towards a standardised brief outcome measure: Psychometric properties and utility of the CORE-OM. British Journal of Psychiatry 180 (2002), 51–60. https://doi.org/10.1192/bjp.180.1.51
Joseph C. Franklin, Jessica D. Ribeiro, Kathryn R. Fox, Kate H. Bentley, Evan M. Kleiman, Xieyining Huang, Katherine M. Musacchio, Adam C. Jaroszewski, Bernard P. Chang, and Matthew K. Nock. 2017. Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research. Psychological Bulletin 143, 2 (2017), 187–232. https://doi.org/10.1037/bul0000084
Wesllei Felipe Heckler, Juliano Varella de Carvalho, and Jorge Luis Victória Barbosa. 2022. Machine learning for suicidal ideation identification: A systematic literature review. Computers in Human Behavior 128 (2022), 107095. https://doi.org/10.1016/j.chb.2021.107095
Wesllei Felipe Heckler, Luan Paris Feijó, Juliano Varella de Carvalho, and Jorge Luis Victória Barbosa. 2023. Thoth: An intelligent model for assisting individuals with suicidal ideation. Expert Systems with Applications (2023), 120918. https://doi.org/10.1016/j.eswa.2023.120918
Shaoxiong Ji, Shirui Pan, Xue Li, Erik Cambria, Guodong Long, and Zi Huang. 2021. Suicidal ideation detection: A review of machine learning methods and applications. IEEE Transactions on Computational Social Systems 8, 1 (2021), 214–226.
Catherine M. McHugh and Matthew M. Large. 2020. Can machine-learning methods really help predict suicide? Current opinion in psychiatry 33, 4 (2020), 369–374. https://doi.org/10.1097/YCO.0000000000000609
NIMH. 2021. Suicide. [link]. Accessed: 2021-03-05.
Lin Padgham and Michael Winikoff. 2004. Developing Intelligent Agent Systems: A Practical Guide. John Wiley Sons Ltd, Melbourne, Australia. https://doi.org/10.1002/0470861223
Derick M. Rentz, Wesllei F. Heckler, and Jorge L. V. Barbosa. 2023. A computational model for assisting individuals with suicidal ideation based on context histories. Universal Access in the Information Society (March 2023). https://doi.org/10.1007/s10209-023-00991-2
Eileen P. Ryan and Maria A. Oquendo. 2020. Suicide Risk Assessment and Prevention: Challenges and Opportunities. Focus 18, 2 (2020), 88–99. https://doi.org/10.1176/appi.focus.20200011
Gustavo Turecki, David A. Brent, David Gunnell, Rory C. O’Connor, Maria A. Oquendo, Jane Pirkis, and Barbara H. Stanley. 2019. Suicide and suicide risk. Nature Reviews: Disease Primers 5, 74 (2019), 1–22. https://doi.org/10.1038/s41572-019-0121-0
Terry Taewoong Um, Franz Michael Josef Pfister, Daniel Pichler, Satoshi Endo, Muriel Lang, Sandra Hirche, Urban Fietzek, and Dana Kulic. 2017. Data Augmentation of Wearable Sensor Data for Parkinson’s Disease Monitoring using Convolutional Neural Networks. CoRR abs/1706.00527 (2017). arXiv:1706.00527 http://arxiv.org/abs/1706.00527
WHO. 2021. Suicide data. [link]. Accessed: 2021-02-15.
Tiago C. Zortea, Seonaid Cleare, Ambrose J. Melson, Karen Wetherall, and Rory C. O’Connor. 2020. Understanding and managing suicide risk. British Medical Bulletin 134, 1 (2020), 73–84. https://doi.org/10.1093/bmb/ldaa013
Jorge Arthur Schneider Aranda, Rodrigo Simon Bavaresco, Juliano Varella de Carvalho, Adenauer Corrêa Yamin, Mauricio Campelo Tavares, and Jorge Luis Victória Barbosa. 2021. A computational model for adaptive recording of vital signs through context histories. Journal of Ambient Intelligence and Humanized Computing (2021), 1–15. https://doi.org/10.1007/s12652-021-03126-8
Fred D. Davis. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems 13, 3 (1989), 319–339. https://doi.org/10.2307/249008
Anind Dey, Gregory Abowd, and Daniel Salber. 2001. A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of Context-Aware Applications. Human-Computer Interaction 16 (2001), 97–166.
Chris Evans, Janice Connell, Michael Barkham, Frank Margison, Graeme McGrath, John Mellor-Clark, and Kerry Audin. 2002. Towards a standardised brief outcome measure: Psychometric properties and utility of the CORE-OM. British Journal of Psychiatry 180 (2002), 51–60. https://doi.org/10.1192/bjp.180.1.51
Joseph C. Franklin, Jessica D. Ribeiro, Kathryn R. Fox, Kate H. Bentley, Evan M. Kleiman, Xieyining Huang, Katherine M. Musacchio, Adam C. Jaroszewski, Bernard P. Chang, and Matthew K. Nock. 2017. Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research. Psychological Bulletin 143, 2 (2017), 187–232. https://doi.org/10.1037/bul0000084
Wesllei Felipe Heckler, Juliano Varella de Carvalho, and Jorge Luis Victória Barbosa. 2022. Machine learning for suicidal ideation identification: A systematic literature review. Computers in Human Behavior 128 (2022), 107095. https://doi.org/10.1016/j.chb.2021.107095
Wesllei Felipe Heckler, Luan Paris Feijó, Juliano Varella de Carvalho, and Jorge Luis Victória Barbosa. 2023. Thoth: An intelligent model for assisting individuals with suicidal ideation. Expert Systems with Applications (2023), 120918. https://doi.org/10.1016/j.eswa.2023.120918
Shaoxiong Ji, Shirui Pan, Xue Li, Erik Cambria, Guodong Long, and Zi Huang. 2021. Suicidal ideation detection: A review of machine learning methods and applications. IEEE Transactions on Computational Social Systems 8, 1 (2021), 214–226.
Catherine M. McHugh and Matthew M. Large. 2020. Can machine-learning methods really help predict suicide? Current opinion in psychiatry 33, 4 (2020), 369–374. https://doi.org/10.1097/YCO.0000000000000609
NIMH. 2021. Suicide. [link]. Accessed: 2021-03-05.
Lin Padgham and Michael Winikoff. 2004. Developing Intelligent Agent Systems: A Practical Guide. John Wiley Sons Ltd, Melbourne, Australia. https://doi.org/10.1002/0470861223
Derick M. Rentz, Wesllei F. Heckler, and Jorge L. V. Barbosa. 2023. A computational model for assisting individuals with suicidal ideation based on context histories. Universal Access in the Information Society (March 2023). https://doi.org/10.1007/s10209-023-00991-2
Eileen P. Ryan and Maria A. Oquendo. 2020. Suicide Risk Assessment and Prevention: Challenges and Opportunities. Focus 18, 2 (2020), 88–99. https://doi.org/10.1176/appi.focus.20200011
Gustavo Turecki, David A. Brent, David Gunnell, Rory C. O’Connor, Maria A. Oquendo, Jane Pirkis, and Barbara H. Stanley. 2019. Suicide and suicide risk. Nature Reviews: Disease Primers 5, 74 (2019), 1–22. https://doi.org/10.1038/s41572-019-0121-0
Terry Taewoong Um, Franz Michael Josef Pfister, Daniel Pichler, Satoshi Endo, Muriel Lang, Sandra Hirche, Urban Fietzek, and Dana Kulic. 2017. Data Augmentation of Wearable Sensor Data for Parkinson’s Disease Monitoring using Convolutional Neural Networks. CoRR abs/1706.00527 (2017). arXiv:1706.00527 http://arxiv.org/abs/1706.00527
WHO. 2021. Suicide data. [link]. Accessed: 2021-02-15.
Tiago C. Zortea, Seonaid Cleare, Ambrose J. Melson, Karen Wetherall, and Rory C. O’Connor. 2020. Understanding and managing suicide risk. British Medical Bulletin 134, 1 (2020), 73–84. https://doi.org/10.1093/bmb/ldaa013
Publicado
23/10/2023
Como Citar
HECKLER, Wesllei Felipe; DE CARVALHO, Juliano Varella; BARBOSA, Jorge Luis Victória.
Thoth: An intelligent model for assisting individuals with suicidal ideation. In: CONCURSO DE TESES E DISSERTAÇÕES - SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 29. , 2023, Ribeirão Preto/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 23-26.
ISSN 2596-1683.
DOI: https://doi.org/10.5753/webmedia_estendido.2023.234461.
