Machine Learning to Assist in Pneumonia Decision Making: A Systematic Review of the Literature
Resumo
Clinical decision support systems is a research area in which Machine Learning (ML) techniques can be applied. Nevertheless, specifically in assisting pneumonia decision making, the use of ML has not been so expressive. To help matters, this work aims to contribute to the evolution of the intersection of such areas by presenting a Systematic Review of the Literature. It provides results which may help to identify, interpret and evaluate how ML techniques have been applied and some research enhancements yet to be done.
Palavras-chave:
Data Mining, Machine Learning, Pneumonia, Systematic Review
Referências
Baechle, C., Agarwal, A., and Zhu, X. Big data driven co-occurring evidence discovery in chronic obstructive pulmonary disease patients. Journal of Big Data vol. 4, pp. 9, 03, 2017.
Ben-Assuli, O. and Padman, R. Analysing repeated hospital readmissions using data mining techniques. Health Systems 7 (2): 120–134, 2018.
Bezemer, T., de Groot, M., Blasse, E., ten Berg, M., Kappen, T. H., Bredenoord, A. L., van Solinge, W. W., Hoefer, I. E., and Haitjema, S. A Human(e) Factor in Clinical Decision Support Systems. Journal of Medical Internet Research 21 (3): e11732, 2019.
Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., and Elhadad, N. Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’15. Association for Computing Machinery, New York, NY, USA, pp. 1721–1730, 2015.
Chen, C., Shi, L., Li, Y., Wang, X., and Yang, S. Disease-specific dynamic biomarkers selected by integrating inflammatory mediators with clinical informatics in ards patients with severe pneumonia. Cell Biol Toxicol, 2016.
Chmielewska, M. Clostridium difficile infection due to pneumonia treatment: Mortality risk models. in: Pokorski M. (eds) Pathobiology of Pulmonary Disorders, 2016.
Chumbita, M., Cillóniz, C., Puerta-Alcalde, P., Moreno-García, E., Sanjuan, G., Garcia-Pouton, N., Soriano, A., Torres, A., and Garcia-Vidal, C. Can artificial intelligence improve the management of pneumonia. Journal of Clinical Medicine 9 (1): 248, Jan, 2020.
de Souza Gomes, E. A. Implementando um Mural Eletrônico em PHP: Uma Aplicação Voltada a uma Instituição de Ensino Superior. Aplicabilidade de Algoritmos de Aprendizado de Máquina para Detecção de Intrusão e Análise de Anomalias de Rede, 2019. Dissertação (especialização) — UFMG, Brasília, DF.
DeLisle, S., Bernard, K., Deepak, J., and Siddiqui, T. Using the electronic medical record to identify community-acquired pneumonia: toward a replicable automated strategy. PLoS One vol. 8, 08, 2013.
Dybå, T. and Dingsøyr, T. Empirical studies of agile software development: A systematic review. Information and Software Technology vol. 50, pp. 833–859, 08, 2008.
Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. From data mining to knowledge discovery in databases. AI magazine 17 (3): 37, 1996.
Ford, E., Carroll, J., Smith, H., Scott, D., and Cassell, J. Extracting information from the text of electronic medical records to improve case detection: A systematic review. Journal of the American Medical Informatics Association vol. 23, pp. ocv180, 02, 2016.
Ge, Y., Wang, Q., Wang, L., Wu, H., Peng, C., Wang, J., Xu, Y., Xiong, G., Zhang, Y., and Yi, Y. Predicting post-stroke pneumonia using deep neural network approaches. International Journal of Medical Informatics vol. 132, pp. 103986, 2019.
Horng, S., Sontag, D., Halpern, Y., Jernite, Y., Shapiro, N., and Nathanson, L. Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning. Vol. 12, 2017.
Khan, W., Zaki, N., and Ali, L. Intelligent pneumonia identification from chest x-rays: A systematic literature review. medRxiv 12 (5): p–p, 2020.
Kitchenham, B. Procedures for Performing Systematic Reviews, 2004.
Lai, H., Chan, P., Lin, H., Chen, Y., Lin, C., and Hsu, J. A web-based decision support system for predicting readmission of pneumonia patients after discharge. In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). pp. 2305–2310, 2018.
Liao, Y.-H., Shih, C.-H., Abbod, M., and Shieh, J.-S. Development of an e-nose system using machine learning methods to predict ventilator-associated pneumonia. Microsyst Technol 2020 , 03, 2020.
Lin, W.-T., Wang, S.-T., Chiang, T.-C., Shi, Y.-X., and yu Chen, W. Abnormal diagnosis of emergency department triage explored with data mining technology: An emergency department at a medical center in taiwan taken as an example. Expert Systems with Applications 37 (4): 2733 – 2741, 2010.
Mitchell, T. Machine Learning. McGraw-Hill International Editions. McGraw-Hill, 1997.
Monard, M. C. and Baranauskas, J. A. Conceitos sobre aprendizado de máquina. In Sistemas Inteligentes Fundamentos e Aplicações, 1 ed. Manole Ltda, Barueri-SP, pp. 89–114, 2003.
Naydenova, E., Tsanas, A., Casals-Pascual, C., and De Vos, M. Smart diagnostic algorithms for automated detection of childhood pneumonia in resource-constrained settings. In 2015 IEEE Global Humanitarian Technology Conference (GHTC). pp. 377–384, 2015.
Naydenova, E., Tsanas, A., Howie, S., Casals-Pascual, C., and de Vos, M. The power of data mining in diagnosis of childhood pneumonia. Journal of The Royal Society Interface vol. 13, pp. 20160266, 07, 2016.
Porat, T., Kostopoulou, O., Woolley, A., and Delaney, B. C. Eliciting user decision requirements for designing computerized diagnostic support for family physicians. Vol. 10, 2016.
Respira, A. A pneumonia. http://www.respira.pt/content/docs/pneumonia.pdf, 2019.
Rozenbauma, M., Mangenc, M.-J., Huijts, S., van der Werf, T., and Postma, M. J. Incidence, direct costs and duration of hospitalization of patients hospitalized with community acquired pneumonia: A nationwide retrospective claims database analysis. Vaccine 3 (28): 3193 – 3199, 2015.
Santana, B. S. Extração e Aplicação de Indicadores no Processo de Recomendação de Recursos Urbanos Utilizando Dados Estruturados e Não-Estruturados. M.S. thesis, Universidade Federal do Rio Grande do Sul, https://lume.ufrgs.br/handle/10183/193897, 2019.
Shimizu, S., Hara, S., and Fushimi, K. Prs55 predicting the risk of in-hospital mortality in adult community-acquired pneumonia patients with machine learning: A retrospective analysis of routinely collected health data. Value in Health vol. 22, pp. S882, 2019. ISPOR Europe 2019.
Ubaid, A., Mirza, F., Baig, M., and Mirza, F. Identifying the relationship between unstable vital signs and intensive care unit (icu) readmissions. Expert Systems with Applications 37 (4): 2733 – 2741, 2010.
Villiers, L., Caspar, Y., Marche, H., Boccoz, S., Maurin, M., Marche, P., Morand, P., Marquette, C., and Corgier, B. Resynplex: Respiratory syndrome linked pathogens multiplex detection and characterization. IRBM 39 (5): 368 – 375, 2018.
Wu, C., Rosenfeld, R., and Clermont, G. Using data-driven rules to predict mortality in severe community acquired pneumonia. PLOS ONE 9 (4): 1–9, 04, 2014.
Ben-Assuli, O. and Padman, R. Analysing repeated hospital readmissions using data mining techniques. Health Systems 7 (2): 120–134, 2018.
Bezemer, T., de Groot, M., Blasse, E., ten Berg, M., Kappen, T. H., Bredenoord, A. L., van Solinge, W. W., Hoefer, I. E., and Haitjema, S. A Human(e) Factor in Clinical Decision Support Systems. Journal of Medical Internet Research 21 (3): e11732, 2019.
Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., and Elhadad, N. Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’15. Association for Computing Machinery, New York, NY, USA, pp. 1721–1730, 2015.
Chen, C., Shi, L., Li, Y., Wang, X., and Yang, S. Disease-specific dynamic biomarkers selected by integrating inflammatory mediators with clinical informatics in ards patients with severe pneumonia. Cell Biol Toxicol, 2016.
Chmielewska, M. Clostridium difficile infection due to pneumonia treatment: Mortality risk models. in: Pokorski M. (eds) Pathobiology of Pulmonary Disorders, 2016.
Chumbita, M., Cillóniz, C., Puerta-Alcalde, P., Moreno-García, E., Sanjuan, G., Garcia-Pouton, N., Soriano, A., Torres, A., and Garcia-Vidal, C. Can artificial intelligence improve the management of pneumonia. Journal of Clinical Medicine 9 (1): 248, Jan, 2020.
de Souza Gomes, E. A. Implementando um Mural Eletrônico em PHP: Uma Aplicação Voltada a uma Instituição de Ensino Superior. Aplicabilidade de Algoritmos de Aprendizado de Máquina para Detecção de Intrusão e Análise de Anomalias de Rede, 2019. Dissertação (especialização) — UFMG, Brasília, DF.
DeLisle, S., Bernard, K., Deepak, J., and Siddiqui, T. Using the electronic medical record to identify community-acquired pneumonia: toward a replicable automated strategy. PLoS One vol. 8, 08, 2013.
Dybå, T. and Dingsøyr, T. Empirical studies of agile software development: A systematic review. Information and Software Technology vol. 50, pp. 833–859, 08, 2008.
Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. From data mining to knowledge discovery in databases. AI magazine 17 (3): 37, 1996.
Ford, E., Carroll, J., Smith, H., Scott, D., and Cassell, J. Extracting information from the text of electronic medical records to improve case detection: A systematic review. Journal of the American Medical Informatics Association vol. 23, pp. ocv180, 02, 2016.
Ge, Y., Wang, Q., Wang, L., Wu, H., Peng, C., Wang, J., Xu, Y., Xiong, G., Zhang, Y., and Yi, Y. Predicting post-stroke pneumonia using deep neural network approaches. International Journal of Medical Informatics vol. 132, pp. 103986, 2019.
Horng, S., Sontag, D., Halpern, Y., Jernite, Y., Shapiro, N., and Nathanson, L. Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning. Vol. 12, 2017.
Khan, W., Zaki, N., and Ali, L. Intelligent pneumonia identification from chest x-rays: A systematic literature review. medRxiv 12 (5): p–p, 2020.
Kitchenham, B. Procedures for Performing Systematic Reviews, 2004.
Lai, H., Chan, P., Lin, H., Chen, Y., Lin, C., and Hsu, J. A web-based decision support system for predicting readmission of pneumonia patients after discharge. In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). pp. 2305–2310, 2018.
Liao, Y.-H., Shih, C.-H., Abbod, M., and Shieh, J.-S. Development of an e-nose system using machine learning methods to predict ventilator-associated pneumonia. Microsyst Technol 2020 , 03, 2020.
Lin, W.-T., Wang, S.-T., Chiang, T.-C., Shi, Y.-X., and yu Chen, W. Abnormal diagnosis of emergency department triage explored with data mining technology: An emergency department at a medical center in taiwan taken as an example. Expert Systems with Applications 37 (4): 2733 – 2741, 2010.
Mitchell, T. Machine Learning. McGraw-Hill International Editions. McGraw-Hill, 1997.
Monard, M. C. and Baranauskas, J. A. Conceitos sobre aprendizado de máquina. In Sistemas Inteligentes Fundamentos e Aplicações, 1 ed. Manole Ltda, Barueri-SP, pp. 89–114, 2003.
Naydenova, E., Tsanas, A., Casals-Pascual, C., and De Vos, M. Smart diagnostic algorithms for automated detection of childhood pneumonia in resource-constrained settings. In 2015 IEEE Global Humanitarian Technology Conference (GHTC). pp. 377–384, 2015.
Naydenova, E., Tsanas, A., Howie, S., Casals-Pascual, C., and de Vos, M. The power of data mining in diagnosis of childhood pneumonia. Journal of The Royal Society Interface vol. 13, pp. 20160266, 07, 2016.
Porat, T., Kostopoulou, O., Woolley, A., and Delaney, B. C. Eliciting user decision requirements for designing computerized diagnostic support for family physicians. Vol. 10, 2016.
Respira, A. A pneumonia. http://www.respira.pt/content/docs/pneumonia.pdf, 2019.
Rozenbauma, M., Mangenc, M.-J., Huijts, S., van der Werf, T., and Postma, M. J. Incidence, direct costs and duration of hospitalization of patients hospitalized with community acquired pneumonia: A nationwide retrospective claims database analysis. Vaccine 3 (28): 3193 – 3199, 2015.
Santana, B. S. Extração e Aplicação de Indicadores no Processo de Recomendação de Recursos Urbanos Utilizando Dados Estruturados e Não-Estruturados. M.S. thesis, Universidade Federal do Rio Grande do Sul, https://lume.ufrgs.br/handle/10183/193897, 2019.
Shimizu, S., Hara, S., and Fushimi, K. Prs55 predicting the risk of in-hospital mortality in adult community-acquired pneumonia patients with machine learning: A retrospective analysis of routinely collected health data. Value in Health vol. 22, pp. S882, 2019. ISPOR Europe 2019.
Ubaid, A., Mirza, F., Baig, M., and Mirza, F. Identifying the relationship between unstable vital signs and intensive care unit (icu) readmissions. Expert Systems with Applications 37 (4): 2733 – 2741, 2010.
Villiers, L., Caspar, Y., Marche, H., Boccoz, S., Maurin, M., Marche, P., Morand, P., Marquette, C., and Corgier, B. Resynplex: Respiratory syndrome linked pathogens multiplex detection and characterization. IRBM 39 (5): 368 – 375, 2018.
Wu, C., Rosenfeld, R., and Clermont, G. Using data-driven rules to predict mortality in severe community acquired pneumonia. PLOS ONE 9 (4): 1–9, 04, 2014.
Publicado
20/10/2020
Como Citar
SILVA, Victor; RAMOS NOVO, Amanda Days; SOUZA, Damires; RÊGO, Alex.
Machine Learning to Assist in Pneumonia Decision Making: A Systematic Review of the Literature. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 8. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 201-208.
ISSN 2763-8944.
DOI: https://doi.org/10.5753/kdmile.2020.11977.
