Mineração de Dados aplicada à Saúde Mental de Estudantes Universitários: Uma Revisão Sistemática
Resumo
Este trabalho apresenta uma revisão sistemática de literatura para verificar o estado atual da aplicação de técnicas de mineração de dados para investigação de transtornos mentais em estudantes universitários. Com a utilização de strings de busca, foram encontrados 187 artigos. Após a leitura dos resumos e aplicação dos critérios de inclusão e exclusão, 53 trabalhos foram selecionados para a leitura completa. Ao final, 25 artigos foram aceitos para a extração dos dados. Assim, foi possível verificar quais os algoritmos e técnicas mais utilizados, os transtornos mais frequentes, os principais instrumentos, as variáveis associadas aos transtornos e nacionalidade das amostras. Dentre os transtornos mais estudados, destaca-se a ansiedade e depressão. Para estes, as técnicas mais utilizadas foram SVM, Inferência Neuro Fuzzy e KNN. Em relação a estudantes universitários brasileiros, pode-se perceber uma escassez de estudos em relação a saúde mental com a utilização de técnicas de mineração de dados.
Referências
Ahujaa, R. and Bangab, A. (2019). Mental stress detection in university students using machine learning algorithms. Procedia Computer Science, 152:349–353.
Alonso, S. G., de La Torre-Díez, I., Hamrioui, S., Lopez-Coronado, M., Barreno, D. C., Nozaleda, L. M., and Franco, M. (2018). Data mining algorithms and techniques in mental health: a systematic review. Journal of medical systems, 42(9):161.
Association, A. P. et al. (2014). DSM-5: Manual diagnostico e estat ístico de transtornos mentais. Artmed Editora.
Beck, A., Steer, R., and Brown, G. (1996). Manual for the beck depression inventory second edition (bdi-ii) psychological corporation. San Antonio, TX.
Bian, J., Barnes, L. E., Chen, G., and Xiong, H. (2017). Early detection of diseases using electronic health records data and covariance-regularized linear discriminant analysis. In 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pages 457–460. IEEE.
BRASIL, O. (2016). Depressao: o que voc e precisa saber.
Cohen, S., Kamarck, T., and Mermelstein, R. (1983). A global measure of perceived stress. Journal of health and social behavior, pages 385–396.
Cui Yuan (2014). Data mining techniques with its application to the dataset of mental health of college students. In 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA), pages 391–393.
da Victoria, M. S., Bravo, A., Felix, A. K., Neves, B. G., Rodrigues, C. B., Ribeiro, C. C. P., Canejo, D., Coelho, D., Sampaio, D., Esteves, I. M., et al. (2015). Níveis de ansiedade e depressao em graduandos da universidade do estado do rio de janeiro (uerj). Encontro: Revista de Psicologia, 16(25):163–175.
Day, V., McGrath, P. J., and Wojtowicz, M. (2013). Internet-based guided self-help for university students with anxiety, depression and stress: a randomized controlled clinical trial. Behaviour research and therapy, 51(7):344–351.
de Rezende, C., Abrao, C., Coelho, E., and da Silva Passos, L. (2008). Prevalencia de sintomas depressivos entre estudantes de medicina da universidade federal de uberlandia. Rev Bras Educ Med, 32(3):315–23.
Derogatis, L. (1992). Administration, scoring and procedures manual-ii (baltimore, md: Clinical psychometric research)(scl-90-r).
Devi, S., Kumar, S., and Kushwaha, G. S. (2016). An adaptive neuro fuzzy inference system for prediction of anxiety of students. In 2016 Eighth International Conference on Advanced Computational Intelligence (ICACI), pages 7–13. IEEE.
Egilmez, B., Poyraz, E., Zhou, W., Memik, G., Dinda, P., and Alshurafa, N. (2017). Ustress: Understanding college student subjective stress using wrist-based passive sensing. In 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pages 673–678. IEEE.
Han, J. and Kamber, M. (2005). Data mining : concepts and techniques. Kaufmann, San Francisco [u.a.].
Hou, Y., Xu, J., Huang, Y., and Ma, X. (2016). A big data application to predict depression in the university based on the reading habits. In 2016 3rd International Conference on Systems and Informatics (ICSAI), pages 1085–1089. IEEE.
Jensen, A. R. (1958). The maudsley personality inventory. Acta Psychologica, 14:314– 325.
Khosrowabadi, R., Quek, C., Ang, K. K., Tung, S. W., and Heijnen, M. (2011). A braincomputer interface for classifying eeg correlates of chronic mental stress. In The 2011 International Joint Conference on Neural Networks, pages 757–762. IEEE.
Lovibond, S. H. and Lovibond, P. F. (1996). Manual for the depression anxiety stress scales. Psychology Foundation of Australia.
Miranda, C. A. C. and Scoppetta, O. (2018). Factorial structure of the patient health questionnaire-9 as a depression screening instrument for university students in cartagena, colombia. Psychiatry research, 269:425–429.
OMS, O. M. d. S. (2002). Relatorio mundial de saúde: Saúde mental, nova concepçáo, nova esperança.
Pintelas, E. G., Kotsilieris, T., Livieris, I. E., and Pintelas, P. (2018). A review of machine learning prediction methods for anxiety disorders. In Proceedings of the 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion, pages 8–15.
Prince, M., Patel, V., Saxena, S., Maj, M., Maselko, J., Phillips, M. R., and Rahman, A. (2007). No health without mental health. The lancet, 370(9590):859–877.
Ritchie, H. and Roser, M. (2019). Mental health. Our World in Data. https://ourworldindata.org/mental-health.
Sinha, A. (1995). Manual for sinha’s comprehensive anxiety test (scat). Agra: National Psychological Corporation.
Spielberger, C. D. (2010). State-trait anxiety inventory. The Corsini encyclopedia of psychology, pages 1–1.
Spitzer, R. L., Kroenke, K., Williams, J. B., Group, P. H. Q. P. C. S., et al. (1999). Validation and utility of a self-report version of prime-md: the phq primary care study. Jama, 282(18):1737–1744.
Srividya, M., Mohanavalli, S., and Bhalaji, N. (2018). Behavioral modeling for mental health using machine learning algorithms. Journal of medical systems, 42(5):88.
WHO et al. (2017). Depression and other common mental disorders: global health estimates. Technical report, World Health Organization.
Yuan, C. (2014). Data mining techniques with its application to the dataset of mental health of college students. In 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA), pages 391–393. IEEE.
Zivin, K., Eisenberg, D., Gollust, S. E., and Golberstein, E. (2009). Persistence of mental health problems and needs in a college student population. Journal of affective disorders, 117(3):180–185.
Alonso, S. G., de La Torre-Díez, I., Hamrioui, S., Lopez-Coronado, M., Barreno, D. C., Nozaleda, L. M., and Franco, M. (2018). Data mining algorithms and techniques in mental health: a systematic review. Journal of medical systems, 42(9):161.
Association, A. P. et al. (2014). DSM-5: Manual diagnostico e estat ístico de transtornos mentais. Artmed Editora.
Beck, A., Steer, R., and Brown, G. (1996). Manual for the beck depression inventory second edition (bdi-ii) psychological corporation. San Antonio, TX.
Bian, J., Barnes, L. E., Chen, G., and Xiong, H. (2017). Early detection of diseases using electronic health records data and covariance-regularized linear discriminant analysis. In 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pages 457–460. IEEE.
BRASIL, O. (2016). Depressao: o que voc e precisa saber.
Cohen, S., Kamarck, T., and Mermelstein, R. (1983). A global measure of perceived stress. Journal of health and social behavior, pages 385–396.
Cui Yuan (2014). Data mining techniques with its application to the dataset of mental health of college students. In 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA), pages 391–393.
da Victoria, M. S., Bravo, A., Felix, A. K., Neves, B. G., Rodrigues, C. B., Ribeiro, C. C. P., Canejo, D., Coelho, D., Sampaio, D., Esteves, I. M., et al. (2015). Níveis de ansiedade e depressao em graduandos da universidade do estado do rio de janeiro (uerj). Encontro: Revista de Psicologia, 16(25):163–175.
Day, V., McGrath, P. J., and Wojtowicz, M. (2013). Internet-based guided self-help for university students with anxiety, depression and stress: a randomized controlled clinical trial. Behaviour research and therapy, 51(7):344–351.
de Rezende, C., Abrao, C., Coelho, E., and da Silva Passos, L. (2008). Prevalencia de sintomas depressivos entre estudantes de medicina da universidade federal de uberlandia. Rev Bras Educ Med, 32(3):315–23.
Derogatis, L. (1992). Administration, scoring and procedures manual-ii (baltimore, md: Clinical psychometric research)(scl-90-r).
Devi, S., Kumar, S., and Kushwaha, G. S. (2016). An adaptive neuro fuzzy inference system for prediction of anxiety of students. In 2016 Eighth International Conference on Advanced Computational Intelligence (ICACI), pages 7–13. IEEE.
Egilmez, B., Poyraz, E., Zhou, W., Memik, G., Dinda, P., and Alshurafa, N. (2017). Ustress: Understanding college student subjective stress using wrist-based passive sensing. In 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pages 673–678. IEEE.
Han, J. and Kamber, M. (2005). Data mining : concepts and techniques. Kaufmann, San Francisco [u.a.].
Hou, Y., Xu, J., Huang, Y., and Ma, X. (2016). A big data application to predict depression in the university based on the reading habits. In 2016 3rd International Conference on Systems and Informatics (ICSAI), pages 1085–1089. IEEE.
Jensen, A. R. (1958). The maudsley personality inventory. Acta Psychologica, 14:314– 325.
Khosrowabadi, R., Quek, C., Ang, K. K., Tung, S. W., and Heijnen, M. (2011). A braincomputer interface for classifying eeg correlates of chronic mental stress. In The 2011 International Joint Conference on Neural Networks, pages 757–762. IEEE.
Lovibond, S. H. and Lovibond, P. F. (1996). Manual for the depression anxiety stress scales. Psychology Foundation of Australia.
Miranda, C. A. C. and Scoppetta, O. (2018). Factorial structure of the patient health questionnaire-9 as a depression screening instrument for university students in cartagena, colombia. Psychiatry research, 269:425–429.
OMS, O. M. d. S. (2002). Relatorio mundial de saúde: Saúde mental, nova concepçáo, nova esperança.
Pintelas, E. G., Kotsilieris, T., Livieris, I. E., and Pintelas, P. (2018). A review of machine learning prediction methods for anxiety disorders. In Proceedings of the 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion, pages 8–15.
Prince, M., Patel, V., Saxena, S., Maj, M., Maselko, J., Phillips, M. R., and Rahman, A. (2007). No health without mental health. The lancet, 370(9590):859–877.
Ritchie, H. and Roser, M. (2019). Mental health. Our World in Data. https://ourworldindata.org/mental-health.
Sinha, A. (1995). Manual for sinha’s comprehensive anxiety test (scat). Agra: National Psychological Corporation.
Spielberger, C. D. (2010). State-trait anxiety inventory. The Corsini encyclopedia of psychology, pages 1–1.
Spitzer, R. L., Kroenke, K., Williams, J. B., Group, P. H. Q. P. C. S., et al. (1999). Validation and utility of a self-report version of prime-md: the phq primary care study. Jama, 282(18):1737–1744.
Srividya, M., Mohanavalli, S., and Bhalaji, N. (2018). Behavioral modeling for mental health using machine learning algorithms. Journal of medical systems, 42(5):88.
WHO et al. (2017). Depression and other common mental disorders: global health estimates. Technical report, World Health Organization.
Yuan, C. (2014). Data mining techniques with its application to the dataset of mental health of college students. In 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA), pages 391–393. IEEE.
Zivin, K., Eisenberg, D., Gollust, S. E., and Golberstein, E. (2009). Persistence of mental health problems and needs in a college student population. Journal of affective disorders, 117(3):180–185.
Publicado
15/09/2020
Como Citar
FARIAS, Mariana; GUSMÃO, Rene; GUSMÃO, Cleonides.
Mineração de Dados aplicada à Saúde Mental de Estudantes Universitários: Uma Revisão Sistemática. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 20. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 49-59.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2020.11501.
