Uso de Aprendizado de Máquina para a Análise de Polaridade Predominante e Transtorno Afetivo Bipolar
Resumo
O Transtorno Afetivo Bipolar (TAB) é uma doença de caráter crônico e incapacitante caracterizada por episódios recorrentes de mania/hipomania e depressão. A polaridade predominante (PP) tem se mostrado como um importante fator clínico e terapêutico no curso do TAB. Neste estudo dados de 855 pacientes com TAB foram usados para realizar uma análise de dados por meio de regras de associação e florestas aleatórias, a fim de determinar a PP de um paciente sem fazer uso do número de episódios passados e suas respectivas polaridades, além de explorar associações entre PP e características sociodemográficas e clínicas.
Palavras-chave:
Transtorno Afetivo Bipolar (TAB), depressão, análise de dados, florestas aleatórias
Referências
Agrawal, R., Imielinski, T., and Swami, A. (1993). Mining association rules between sets of items in large databases. volume 22, pages 207–216. ACM SIGMOD International conference of Management of data, Washington, D.C.
Angst, J., Felder, W., Frey, R., and Stassen, H. (1978). The course of affective disorders. European Archives of Psychiatry and Clinical Neuroscience, 226:57–64.
em:.
Azorin, J. M., Adida, M., and Belzeaux, R. (2015). Predominant polarity in bipolar disorders: Further evidence for the role of affective temperaments. Journal of Affective Disorders, 182:57–63.
Belizario, G. O., Borges-Ju´nior, R. G., Salvini, R., Lafer, B., and Dias, R. S. (2019). Predominant polarity classification and associated clinical variables in bipolar disorder: A machine learning approach. Journal of Affective Disorders, 245:279–282.
Belizario, G. O., Silva, M., and Lafer, B. (2018). Impact of predominant polarity on long-term outcome in bipolar disorder: A 7-year longitudinal cohort study. Journal of Affective Disorders, 241:37–40.
Borges-Júnior, R. G. (2018). Aprendizado de Máquina para Análise de Recaída para Depressão em Pacientes com Transtorno Bipolar. Master’s thesis, Instituto de Informática - Universidade Federal de Goiás.
Breiman, L. (2001). Random Forests. Machine Learning, 45(1):5–32.
Carvalho, A. F., McIntyre, R. S., Dimelis, D., Gonda, X., Berk, M., Nunes-Neto, P. R., Cha, D., Hyphantis, T. N., Angst, J., and Fountoulakis, K. N. (2014). Predominant polarity as a course specifier for bipolar disorder: A systematic review. Journal of Affective Disorders, 163:56–64.
Carvalho, A. F., Quevedo, J., McIntyre, R. S., Soeiro-de Souza, M. G., Fountoulakis,
K. N., Berk, M., Hyphantis, T. N., and Vieta, E. (2015). Treatment Implications of Predominant Polarity Index: A Comprehensive Review. International Journal of Neu- ropsychopharmacology, pages 1–11.
Colom, F., Vieta, E., Daban, C., Pacchiarotti, I., and Sa´nchez-Moreno, J. (2006). Clinical and therapeutic implications of predominant polarity in bipolar disorder. Journal of Affective Disorders, 93:13–17.
Faceli, K., Lorena, A. C., Gama, J., and Carvalho, A. C. P. L. F. (2011). Inteligência Artificial uma abordagem de aprendizado de máquina. LTC.
González-Pinto, A., Alberich, S., Barbeito, S., Alonso, M., Vieta, E., Martínez-Arán, A., Saenz, M., and López, P. (2010). Different profile of substance abuse in relation to predominant polarity in bipolar disorder. Journal of Affective Disorders, 124:250–255.
Kantardzic, M. (2011). Data Mining: Concepts, Models, Methods, and Algorithms. 2. ed. Institute of Electrical and Electronics Engineers. Wiley.
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In International joint Conference on artificial intelligence, volume 14, pages 1137–1145.
Liaw, A. and Wiener, M. (2002). Classification and Regression by randomForest. R News, 2(3):18–22.
Nivoli, A. M. A., Pacchiarotti, I., Rosa, A. R., Popovic, D., Murru, A., Valenti, M., Bon- nin, C. M., Grande, I., Sa´nchez-Moreno, J., Vieta, E., and Colom, F. (2011). Gender differences in a cohort study of 604 bipolar patients: The role of predominant polarity. Journal of Affective Disorders, 133:443–449.
Popovic, D., Torrent, C., Goikolea, J. M., Cruz, N., Sa´nchez-Moreno, J., Gonza´lez-Pinto, A., and Vieta, E. (2014). Clinical implications of predominant polarity and the pola- rity index in bipolar disorder: a naturalistic study. Acta Psychiatrica Scandinavica, 129:366–374.
Quinlan, J. R. (1986). Induction of Decision Trees. Machine Learning, (1):81–106.
Russel, S. and Norvig, P. (2002). Artificial Intelligence: A Modern Approach. Prentice Hall Series, 2 edition.
Sachs, G. S., Thase, M. E., Otto, M. W., Bauer, M., Miklowitz, D., Wisniewski, S. R., La- vori, P., Lebowitz, B., Rudorfer, M., Frank, E., Nierenberg, A. A., Fava, M., Bowden, C., Ketter, T., Marangell, L., Calabrese, J., Kupfer, D., and Rosenbaum, J. F. (2003). Rationale, design, and methods of the systematic treatment enhancement program for bipolar disorder (STEP-BD). Biological Psychiatry, 53(11):1028–1042.
Spackman, K. A. (1989). Signal detection theory: valuable tools for evaluation inductive learning. In 6th International Workshop on Machine Learning, pages 160–163.
Tan, P. N., Steinbach, M., and Kumar, V. (2006). Introduction to DATA MINING. Pearson Education, 1 edition.
Vidal-Rubio, S. L., Balanzá-Martínez, V., Cuenca, M., and Vila-France´s, J. (2018). Du- ration of euthymia and predominant polarity in bipolar disorder. Journal of Affective Disorders, 241:356–359.
Volkert, J., Zierhut, K. C., Schiele, M. A., Wenzel, M., Kopf, J., Kittel-Schneider, S., and Reif, A. (2014). Predominant polarity in bipolar disorder and validation of the polarity index in a German sample. BMC Psychiatry, 14:14–21.
Angst, J., Felder, W., Frey, R., and Stassen, H. (1978). The course of affective disorders. European Archives of Psychiatry and Clinical Neuroscience, 226:57–64.
em:
Azorin, J. M., Adida, M., and Belzeaux, R. (2015). Predominant polarity in bipolar disorders: Further evidence for the role of affective temperaments. Journal of Affective Disorders, 182:57–63.
Belizario, G. O., Borges-Ju´nior, R. G., Salvini, R., Lafer, B., and Dias, R. S. (2019). Predominant polarity classification and associated clinical variables in bipolar disorder: A machine learning approach. Journal of Affective Disorders, 245:279–282.
Belizario, G. O., Silva, M., and Lafer, B. (2018). Impact of predominant polarity on long-term outcome in bipolar disorder: A 7-year longitudinal cohort study. Journal of Affective Disorders, 241:37–40.
Borges-Júnior, R. G. (2018). Aprendizado de Máquina para Análise de Recaída para Depressão em Pacientes com Transtorno Bipolar. Master’s thesis, Instituto de Informática - Universidade Federal de Goiás.
Breiman, L. (2001). Random Forests. Machine Learning, 45(1):5–32.
Carvalho, A. F., McIntyre, R. S., Dimelis, D., Gonda, X., Berk, M., Nunes-Neto, P. R., Cha, D., Hyphantis, T. N., Angst, J., and Fountoulakis, K. N. (2014). Predominant polarity as a course specifier for bipolar disorder: A systematic review. Journal of Affective Disorders, 163:56–64.
Carvalho, A. F., Quevedo, J., McIntyre, R. S., Soeiro-de Souza, M. G., Fountoulakis,
K. N., Berk, M., Hyphantis, T. N., and Vieta, E. (2015). Treatment Implications of Predominant Polarity Index: A Comprehensive Review. International Journal of Neu- ropsychopharmacology, pages 1–11.
Colom, F., Vieta, E., Daban, C., Pacchiarotti, I., and Sa´nchez-Moreno, J. (2006). Clinical and therapeutic implications of predominant polarity in bipolar disorder. Journal of Affective Disorders, 93:13–17.
Faceli, K., Lorena, A. C., Gama, J., and Carvalho, A. C. P. L. F. (2011). Inteligência Artificial uma abordagem de aprendizado de máquina. LTC.
González-Pinto, A., Alberich, S., Barbeito, S., Alonso, M., Vieta, E., Martínez-Arán, A., Saenz, M., and López, P. (2010). Different profile of substance abuse in relation to predominant polarity in bipolar disorder. Journal of Affective Disorders, 124:250–255.
Kantardzic, M. (2011). Data Mining: Concepts, Models, Methods, and Algorithms. 2. ed. Institute of Electrical and Electronics Engineers. Wiley.
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In International joint Conference on artificial intelligence, volume 14, pages 1137–1145.
Liaw, A. and Wiener, M. (2002). Classification and Regression by randomForest. R News, 2(3):18–22.
Nivoli, A. M. A., Pacchiarotti, I., Rosa, A. R., Popovic, D., Murru, A., Valenti, M., Bon- nin, C. M., Grande, I., Sa´nchez-Moreno, J., Vieta, E., and Colom, F. (2011). Gender differences in a cohort study of 604 bipolar patients: The role of predominant polarity. Journal of Affective Disorders, 133:443–449.
Popovic, D., Torrent, C., Goikolea, J. M., Cruz, N., Sa´nchez-Moreno, J., Gonza´lez-Pinto, A., and Vieta, E. (2014). Clinical implications of predominant polarity and the pola- rity index in bipolar disorder: a naturalistic study. Acta Psychiatrica Scandinavica, 129:366–374.
Quinlan, J. R. (1986). Induction of Decision Trees. Machine Learning, (1):81–106.
Russel, S. and Norvig, P. (2002). Artificial Intelligence: A Modern Approach. Prentice Hall Series, 2 edition.
Sachs, G. S., Thase, M. E., Otto, M. W., Bauer, M., Miklowitz, D., Wisniewski, S. R., La- vori, P., Lebowitz, B., Rudorfer, M., Frank, E., Nierenberg, A. A., Fava, M., Bowden, C., Ketter, T., Marangell, L., Calabrese, J., Kupfer, D., and Rosenbaum, J. F. (2003). Rationale, design, and methods of the systematic treatment enhancement program for bipolar disorder (STEP-BD). Biological Psychiatry, 53(11):1028–1042.
Spackman, K. A. (1989). Signal detection theory: valuable tools for evaluation inductive learning. In 6th International Workshop on Machine Learning, pages 160–163.
Tan, P. N., Steinbach, M., and Kumar, V. (2006). Introduction to DATA MINING. Pearson Education, 1 edition.
Vidal-Rubio, S. L., Balanzá-Martínez, V., Cuenca, M., and Vila-France´s, J. (2018). Du- ration of euthymia and predominant polarity in bipolar disorder. Journal of Affective Disorders, 241:356–359.
Volkert, J., Zierhut, K. C., Schiele, M. A., Wenzel, M., Kopf, J., Kittel-Schneider, S., and Reif, A. (2014). Predominant polarity in bipolar disorder and validation of the polarity index in a German sample. BMC Psychiatry, 14:14–21.
Publicado
22/11/2019
Como Citar
DE CASTRO, Giovanna Aguiar; SALVINI, Rogério ; BELIZARIO, Gabriel ; DIAS, Rodrigo da Silva.
Uso de Aprendizado de Máquina para a Análise de Polaridade Predominante e Transtorno Afetivo Bipolar. In: ESCOLA REGIONAL DE INFORMÁTICA DE GOIÁS (ERI-GO), 7. , 2019, Goiânia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 169-182.
