Identificando Sinais de Estresse Pós-traumático Utilizando Dados Fisiológicos e Técnicas de Regressão
Resumo
Diversos estudos propõem aplicar técnicas computacionais à área da saúde para auxiliar médicos em seus diagnósticos. Este artigo propõe aplicar mineração de dados para identificar sinais de estresse pós-traumático baseado em dados fisiológicos. Para avaliar a proposta, foram usados dados de indivíduos que vivenciaram eventos traumáticos. Estes indivíduos tiveram seus sinais de frequência cardíaca e condutância da pele coletados durante a visualização de imagens com e sem cenas violentas. Foram utilizados algoritmos de regressão para predizer os valores da escala PCL-C dos indivíduos e identificar uma possível correlação entre os dados e a escala. O algoritmo IBk (k = 4) obteve o melhor coeficiente de correlação 0,4164 (p-value = 0,001).
Referências
Alves, R. d. C. S. (2012). Modulação da Frequencia Cardíaca pela Visualização de Estímulos de Ameaça. Master’s thesis, Biomedical Institute, Federal Fluminense University, Niteroi, RJ, Brazil.
Association, A. P. et al. (2013). Diagnostic and statistical manual of mental disorders (DSM-5). American Psychiatric Pub, 5 edition.
Bliese, P. D., Wright, K. M., Adler, A. B., Cabrera, O., Castro, C. A., and Hoge, C. W. (2008). Validating the primary care posttraumatic stress disorder screen and the posttraumatic stress disorder checklist with soldiers returning from combat. Journal of Consulting and Clinical Psychology, 76(2):272.
Galatzer-Levy, I. R., Karstoft, K.-I., Statnikov, A., and Shalev, A. Y. (2014). Quantitative forecasting of PTSD from early trauma responses: A machine learning application. Journal of Psychiatric Research, 59:68–76.
Harrington, T. and Newman, E. (2007). The psychometric utility of two self-report measures of PTSD among women substance users. Addictive Behaviors, 32(12):2788–2798.
Karstoft, K.-I., Galatzer-Levy, I. R., Statnikov, A., Li, Z., and Shalev, A. Y. (2015). Bridging a translational gap: using machine learning to improve the prediction of PTSD. BMC Psychiatry, 15(1):30.
Lanius, R. A., Williamson, P. C., Boksman, K., Densmore, M., Gupta, M., et al. (2002). Brain activation during script-driven imagery induced dissociative responses in PTSD: a functional magnetic resonance imaging investigation. Biological Psychiatry, 52(4):305–311.
Leightley, D., Williamson, V., Darby, J., and Fear, N. T. (2019). Identifying probable post-traumatic stress disorder: applying supervised machine learning to data from a UK military cohort. Journal of Mental Health, 28(1):34–41.
Monson, C. M., Gradus, J. L., Young-Xu, Y., Schnurr, P. P., Price, J. L., et al. (2008). Change in posttraumatic stress disorder symptoms: do clinicians and patients agree? Psychological Assessment, 20(2):131.
Omurca, S. ˙I. and Ekinci, E. (2015). An alternative evaluation of post traumatic stress disorder with machine learning methods. In 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA), pages 1–7. IEEE.
Portugal, L. C., Rosa, M. J., Rao, A., Bebko, G., Bertocci, M. A., et al. (2016). Can emotional and behavioral dysregulation in youth be decoded from functional neuroimaging? PLOS ONE, 11(1):e0117603.
Regier, D. A., Farmer, M. E., Rae, D. S., Locke, B. Z., Keith, S. J., et al. (1990). Comorbidity of mental disorders with alcohol and other drug abuse: results from the Epidemiologic Catchment Area (ECA) study. JAMA, 264(19):2511–2518.
Saxe, G. N., Ma, S., Ren, J., and Aliferis, C. (2017). Machine learning methods to predict child posttraumatic stress: a proof of concept study. BMC Psychiatry, 17(1):223.
Shalev, A. Y., Sahar, T., Freedman, S., Peri, T., Glick, N., et al. (1998). A prospective study of heart rate response following trauma and the subsequent development of posttraumatic stress disorder. Archives of General Psychiatry, 55(6):553–559.
Weathers, F. W., Litz, B. T., Herman, D. S., Huska, J. A., Keane, T. M., et al. (1993). The PTSD Checklist (PCL): Reliability, validity, and diagnostic utility. In Annual Convention of The International Society for Traumatic Stress Studies, San Antonio, TX, volume 462. San Antonio, TX.
Weathers, F. W., Litz, B. T., Keane, T. M., Palmieri, P. A., Marx, B. P., et al. (2013). The PTSD checklist for DSM-5 (PCL-5). Scale available from the National Center for PTSD at www.ptsd.va.gov.
Witten, I. H., Frank, E., Hall, M. A., and Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 4 edition.
Association, A. P. et al. (2013). Diagnostic and statistical manual of mental disorders (DSM-5). American Psychiatric Pub, 5 edition.
Bliese, P. D., Wright, K. M., Adler, A. B., Cabrera, O., Castro, C. A., and Hoge, C. W. (2008). Validating the primary care posttraumatic stress disorder screen and the posttraumatic stress disorder checklist with soldiers returning from combat. Journal of Consulting and Clinical Psychology, 76(2):272.
Galatzer-Levy, I. R., Karstoft, K.-I., Statnikov, A., and Shalev, A. Y. (2014). Quantitative forecasting of PTSD from early trauma responses: A machine learning application. Journal of Psychiatric Research, 59:68–76.
Harrington, T. and Newman, E. (2007). The psychometric utility of two self-report measures of PTSD among women substance users. Addictive Behaviors, 32(12):2788–2798.
Karstoft, K.-I., Galatzer-Levy, I. R., Statnikov, A., Li, Z., and Shalev, A. Y. (2015). Bridging a translational gap: using machine learning to improve the prediction of PTSD. BMC Psychiatry, 15(1):30.
Lanius, R. A., Williamson, P. C., Boksman, K., Densmore, M., Gupta, M., et al. (2002). Brain activation during script-driven imagery induced dissociative responses in PTSD: a functional magnetic resonance imaging investigation. Biological Psychiatry, 52(4):305–311.
Leightley, D., Williamson, V., Darby, J., and Fear, N. T. (2019). Identifying probable post-traumatic stress disorder: applying supervised machine learning to data from a UK military cohort. Journal of Mental Health, 28(1):34–41.
Monson, C. M., Gradus, J. L., Young-Xu, Y., Schnurr, P. P., Price, J. L., et al. (2008). Change in posttraumatic stress disorder symptoms: do clinicians and patients agree? Psychological Assessment, 20(2):131.
Omurca, S. ˙I. and Ekinci, E. (2015). An alternative evaluation of post traumatic stress disorder with machine learning methods. In 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA), pages 1–7. IEEE.
Portugal, L. C., Rosa, M. J., Rao, A., Bebko, G., Bertocci, M. A., et al. (2016). Can emotional and behavioral dysregulation in youth be decoded from functional neuroimaging? PLOS ONE, 11(1):e0117603.
Regier, D. A., Farmer, M. E., Rae, D. S., Locke, B. Z., Keith, S. J., et al. (1990). Comorbidity of mental disorders with alcohol and other drug abuse: results from the Epidemiologic Catchment Area (ECA) study. JAMA, 264(19):2511–2518.
Saxe, G. N., Ma, S., Ren, J., and Aliferis, C. (2017). Machine learning methods to predict child posttraumatic stress: a proof of concept study. BMC Psychiatry, 17(1):223.
Shalev, A. Y., Sahar, T., Freedman, S., Peri, T., Glick, N., et al. (1998). A prospective study of heart rate response following trauma and the subsequent development of posttraumatic stress disorder. Archives of General Psychiatry, 55(6):553–559.
Weathers, F. W., Litz, B. T., Herman, D. S., Huska, J. A., Keane, T. M., et al. (1993). The PTSD Checklist (PCL): Reliability, validity, and diagnostic utility. In Annual Convention of The International Society for Traumatic Stress Studies, San Antonio, TX, volume 462. San Antonio, TX.
Weathers, F. W., Litz, B. T., Keane, T. M., Palmieri, P. A., Marx, B. P., et al. (2013). The PTSD checklist for DSM-5 (PCL-5). Scale available from the National Center for PTSD at www.ptsd.va.gov.
Witten, I. H., Frank, E., Hall, M. A., and Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 4 edition.
Publicado
15/09/2020
Como Citar
PONTE JUNIOR, Luiz; MUCHALUAT-SAADE, Débora; PLASTINO, Alexandre; ALVES, Rita; PORTUGAL, Liana; OLIVEIRA, Leticia; PEREIRA, Mirtes.
Identificando Sinais de Estresse Pós-traumático Utilizando Dados Fisiológicos e Técnicas de Regressão. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 20. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 143-154.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2020.11509.
