Previsão de internações por doença pulmonar obstrutiva crônica através da análise de dados ambulatoriais com técnicas de aprendizado de máquina
Resumo
Internações hospitalares representam o maior custo médico-hospitalar per capita para operadoras de planos de saúde públicos e privadas. Por isso aumentam consideravelmente o custo de assistência médica e representam a maior parte do custo das operadoras. Muitas dessas internações podem ser preditas através do uso de técnicas de aprendizado de máquina aplicadas na análise de dados de histórico ambulatorial dos pacientes, já que estes pacientes possuem dados ambulatoriais disponíveis nos vários sistemas de informações de saúde publica e privada. Uma dessas hospitalizações é a internação por Doença Pulmonar Obstrutiva Crônica (DPOC). Este estudo busca encontrar padrões em dados ambulatoriais de DPOC gerando modelos para a previsão de internação baseados em algoritmos de aprendizado de máquina.
Palavras-chave:
Aprendizado de máquina, Doença Pulmonar Obstrutiva Crônica (DPOC), planos de saúde públicos, privadas
Referências
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A. (2014). Classification of exacerbation episodes in chronic obstructive pulmonary disease patients. Methods Inf Med, 53(2):108–114.
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Noronha, J. C. (2008). Ministério da saúde / secretaria de atenção à saúde - portaria nº 221, de 17 de abril de 2008. http://bvsms.saude.gov.br/bvs/saudelegis/ sas/2008/prt0221_17_04_2008.html. Acessado em: 14/07/2018 23:30.
Orchard, P., Agakova, A., Pinnock, H., Burton, C. D., Sarran, C., Agakov, F., and Mc- Kinstry, B. (2018). Improving Obstructive Pulmonary Disease: Application of Machine Learning to Telemonitoring Data. J. Med. Internet Res., 20(9):e263.Prediction of Risk of Hospital Admission in Chronic
Pang-Ning Tan, Michael Stenbach, V. K. (2005). Introduction to Data Mining. Addison- Wesley, 3th edition.
Sarkar, C. and Srivastava, J. (2013). Impact of density of lab data in ehr for prediction of potentially preventable events. In 2013 IEEE International Conference on Healthcare Informatics (ICHI), pages 529–534. IEEE.
Spathis, D. and Vlamos, P. (2017). Diagnosing asthma and chronic obstructive pulmonary disease with machine learning. Health Informatics J, page 1460458217723169.
Stefanie Weber, J. H. (2018). Who - international classification of diseases, 11th revision (icd-11). http://www.who.int/classifications/icd/en/#. (Acessado em: 14/07/2018 13:34).
Swaminathan, S., Qirko, K., Smith, T., Corcoran, E., Wysham, N. G., Bazaz, G., Kappel, G., and Gerber, A. N. (2017). A machine learning approach to triaging patients with chronic obstructive pulmonary disease. PLoS ONE, 12(11):e0188532.
Theodoridis, S. and Koutroumbas, K. (2008). Pattern Recognition. Academic Press. VCMH/IESS (2018). Índice de variação de custos médico-hospitalares. https://www.iess.org.br/cms/rep/historico_vcmh.pdf. (Accessed on 05/24/2019).
Xie, Y., Redmond, S. J., Mohktar, M. S., Shany, T., Basilakis, J., Hession, M., and Lovell, N. H. (2013). Prediction of chronic obstructive pulmonary disease exacerbation using physiological time series patterns. Conf Proc IEEE Eng Med Biol Soc, 2013:6784– 6787.
Billings, J., Zeitel, L., Lukomnik, J., Carey, T. S., Blank, A. E., and Newman, L. (1993). Impact of socioeconomic status on hospital use in new york city. Health affairs, 12(1):162–173.
Desikan, P., Srivastava, N., Winden, T., Lindquist, T., Britt, H., and Srivastava, J. (2012). Early prediction of potentially preventable events in ambulatory care sensitive admis- sions from clinical data. In Healthcare Informatics, Imaging and Systems Biology (HISB), 2012 IEEE Second International Conference on, pages 124–124. IEEE.
Dias, A., Gorzelniak, L., Schultz, K., Wittmann, M., Rudnik, J., Jorres, R., and Horsch,
A. (2014). Classification of exacerbation episodes in chronic obstructive pulmonary disease patients. Methods Inf Med, 53(2):108–114.
Institute, N. C. (2003). Fourth National Forum on Biomedical Imaging in Oncology — Reports & Publications — Cancer Imaging Program (CIP). (Accessed on 05/29/2019).
Morimoto, T. and Costa, J. S. D. d. (2017). Hospitalization for primary care susceptible conditions, health spending and family health strategy: an analysis of trends. Ciencia & saude coletiva, 22(3):891–900.
Noronha, J. C. (2008). Ministério da saúde / secretaria de atenção à saúde - portaria nº 221, de 17 de abril de 2008. http://bvsms.saude.gov.br/bvs/saudelegis/ sas/2008/prt0221_17_04_2008.html. Acessado em: 14/07/2018 23:30.
Orchard, P., Agakova, A., Pinnock, H., Burton, C. D., Sarran, C., Agakov, F., and Mc- Kinstry, B. (2018). Improving Obstructive Pulmonary Disease: Application of Machine Learning to Telemonitoring Data. J. Med. Internet Res., 20(9):e263.Prediction of Risk of Hospital Admission in Chronic
Pang-Ning Tan, Michael Stenbach, V. K. (2005). Introduction to Data Mining. Addison- Wesley, 3th edition.
Sarkar, C. and Srivastava, J. (2013). Impact of density of lab data in ehr for prediction of potentially preventable events. In 2013 IEEE International Conference on Healthcare Informatics (ICHI), pages 529–534. IEEE.
Spathis, D. and Vlamos, P. (2017). Diagnosing asthma and chronic obstructive pulmonary disease with machine learning. Health Informatics J, page 1460458217723169.
Stefanie Weber, J. H. (2018). Who - international classification of diseases, 11th revision (icd-11). http://www.who.int/classifications/icd/en/#. (Acessado em: 14/07/2018 13:34).
Swaminathan, S., Qirko, K., Smith, T., Corcoran, E., Wysham, N. G., Bazaz, G., Kappel, G., and Gerber, A. N. (2017). A machine learning approach to triaging patients with chronic obstructive pulmonary disease. PLoS ONE, 12(11):e0188532.
Theodoridis, S. and Koutroumbas, K. (2008). Pattern Recognition. Academic Press. VCMH/IESS (2018). Índice de variação de custos médico-hospitalares. https://www.iess.org.br/cms/rep/historico_vcmh.pdf. (Accessed on 05/24/2019).
Xie, Y., Redmond, S. J., Mohktar, M. S., Shany, T., Basilakis, J., Hession, M., and Lovell, N. H. (2013). Prediction of chronic obstructive pulmonary disease exacerbation using physiological time series patterns. Conf Proc IEEE Eng Med Biol Soc, 2013:6784– 6787.
Publicado
22/11/2019
Como Citar
SILVA, Zilmar S.; SALVINI, Rogério .
Previsão de internações por doença pulmonar obstrutiva crônica através da análise de dados ambulatoriais com técnicas de aprendizado de máquina. In: ESCOLA REGIONAL DE INFORMÁTICA DE GOIÁS (ERI-GO), 7. , 2019, Goiânia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 129-141.
