Identification of factors related to complications in myocardial revascularization surgery: an approach with multi-target association rules networks
DOI:
https://doi.org/10.33448/rsd-v11i15.37638Keywords:
Association rules; Association rules networks; Coronary; Coronary artery bypass surgery; Data mining; Multi-target; Multi-target.Abstract
Myocardial revascularization surgery is one of the recommended approaches for the treatment of chronic coronary disease. Several complications related to mortality, sequelae, length of stay, and hospital costs are also associated with this procedure. Death rates and complications depend on the characteristics of each patient. Knowing the factors related to hospital mortality and complications is paramount to improving outcomes. Association Rules Mining may support the discovery of those factors. In this work we propose a new approach, called Multi-target Association Rules Network (MTARN), to analyze association rules based on networks with a simultaneous focus on two parameters. The use of association rules networks aids the analysis of a high number of association rules and the multi-target strategy allows a complete exploration, explaining which factors directly influence the analyzed set. We evaluated our approach in conjunction with domain experts and compared it to two other approaches: Decision Trees and Filtered-ARNs, a single target approach based on networks for pattern visualization. The results indicates that MTARNs optimize the discovery of knowledge directly linked to complication and death factors in patients undergoing coronary artery bypass grafting. These parameters may be used in the construction of an intelligent monitoring system to aid myocardial revascularization patients.
References
Aggarwal, C. C. (2015). Data Mining: The Textbook. Springer International Publishing. Springer. https://doi.org/10.1007/978-3-319-14142-8
Aggarwal, C. C., Procopiuc, C., & Yu, P. S. (2002). Finding localized associations in market basket data. IEEE Transactions on Knowledge and Data Engineering, 14(1), 51–62. https://doi.org/10.1109/69.979972
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., & Verkamo, a I. (1996). Fast discovery of association rules. Advances in Knowledge Discovery and Data Mining, 12, 307–328.
Agrawal, R., Srikant, R., & others. (1994). Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases, VLDB (Vol. 1215, pp. 487–499).
Agrawal, R., Imielinski, T., & Swami, A. (1994). Mining Association Rules between Sets of Items in Large Databases. Special Interest Group on Management of Data, 22(2), 22(2), 207–216. https://doi.org/10.1145/170036.170072
de Almeida, D. V., de Oliveira, K. F., de Oliveira, J. F., Pires, N. L., & Filgueira, V. da S. A. (2013). Most frequent Nursing diagnostics in patients hospitalized in the Coronary Intensive Care Unit. Arquivos Médicos Dos Hospitais e Da Faculdade de Ciências Médicas Da Santa Casa de São Paulo, 58, 64–69.
Amato, V. L., Timerman, A., Paes, A. T., Baltar, V. T., Farsky, P. S., Farran, J. A., & Sousa, J. E. M. R. (2004). Immediate results of myocardial revascularization. Comparison between men and women. Arquivos Brasileiros de Cardiologia, 83(12), 14–20. https://doi.org//S0066-782X2004001900004
Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: An Open Source Software for Exploring and Manipulating Networks. In Third International AAAI Conference on Weblogs and Social Media (pp. 361–362). https://doi.org/10.1136/qshc.2004.010033
Calçada, D. B., & Rezende, S. O. (2019). Filtered-ARN: Asymmetric objective measures applied to filter Association Rules Networks. CLEI Electronic Journal, 22(3), 1–16. https://doi.org/10.19153/cleiej.22.3.2
Calçada, D. B., de Padua, R., & Rezende, S. O. (2018). Asymmetric Objective Measures applied to Filter Association Rules Networks. In Latin American Computing Conference - CLEI-LACLO (pp. 1–10). São Paulo.
D’Agostino, R. S., Jacobs, J. P., Badhwar, V., Fernandez, F. G., Paone, G., Wormuth, D. W., & Shahian, D. M. (2018). The Society of Thoracic Surgeons Adult Cardiac Surgery Database: 2018 Update on Outcomes and Quality. Annals of Thoracic Surgery, 105(1), 15–23. https://doi.org/10.1016/j.athoracsur.2017.10.035
Delgado, E. R., Rodríguez-Mazahua, L., Peláez-Camarena, S. G., Guzmán, J. A. P., & López-Chau, A. (2018). Association Analysis of Medical Opinions About the Non-realization of Autopsies in a Mexican Hospital. Scientific Programming, 2018, 233–251. https://doi.org/10.1007/978-3-319-56871-3_12
Dosilovic, F. K., Brcic, M., & Hlupic, N. (2018). Explainable artificial intelligence: A survey. In 41st International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2018 (pp. 210–215). Croatian Society MIPRO. https://doi.org/10.23919/MIPRO.2018.8400040
DuBois, D., & DuBois, E. F. (1916). A formula to estimate the approximate surface area if height and weight be known. Arch. Intern. Med., 17, 863–871.
Egito, J. G. T. Do, Abboud, C. S., Oliveira, A. P. V. De, Máximo, C. A. G., Montenegro, C. M., Amato, V. L., & Farsky, P. S. (2013). Clinical evolution of mediastinitis in patients undergoing adjuvant hyperbaric oxygen therapy after coronary artery bypass surgery. Einstein J, 11(3), 345–349.
Fukuda, T., Morimoto, Y., Morishita, S., & Tokuyama, T. (1996). Data Mining Using Two-Dimensional Optimized Association Rules: Scheme, Algorithms, and Visualization. In Proceedings of the 1996 ACM SIGMOD international conference on Management of data (pp. 13–23).
Hannan, E. L., Zhong, Y., Lahey, S. J., Culliford, A. T., Gold, J. P., Smith, C. R., & Wechsler, A. (2011). 30-Day readmissions after coronary artery bypass graft surgery in New York State. JACC: Cardiovascular Interventions, 4(5), 569–576. https://doi.org/10.1016/j.jcin.2011.01.010
Hossain, M. E., Uddin, S., & Khan, A. (2021). Network analytics and machine learning for predictive risk modelling of cardiovascular disease in patients with type 2 diabetes. Expert Systems with Applications, 164(September 2020), 113918. https://doi.org/10.1016/j.eswa.2020.113918
Kansagara, D., Englander, H., Salanitro, A., Kagen, D., Theobald, C., & Freeman, M. (2011). Risk prediction models for hospital readmission: a systematic review. JAMA, 306(15), 1688–1698.
Kauw, D., Koole, M. A. C., Winter, M. M., Dohmen, D. A. J., Tulevski, I. I., Blok, S., & Schuuring, M. J. (2019). Advantages of mobile health in the management of adult patients with congenital heart disease. International Journal of Medical Informatics, 132(October), 104011. https://doi.org/10.1016/j.ijmedinf.2019.104011
Kojuri, J., Boostani, R., Dehghani, P., Nowroozipour, F., & Saki, N. (2015). Prediction of acute myocardial infarction with artificial neural networks in patients with nondiagnostic electrocardiogram. Journal of Cardiovascular Disease Research, 6(2), 51–59. https://doi.org/10.5530/jcdr.2015.2.2
Kuo, Y.-H., Chan, N. B., Leung, J. M. Y., Meng, H., So, A. M.-C., Tsoi, K. K. F., & Graham, C. A. (2020). An Integrated Approach of Machine Learning and Systems thinking for Waiting Time Prediction in an Emergency Department. International Journal of Medical Informatics, 104143, 1–30. https://doi.org/10.1016/j.neubiorev.2019.07.019
Lakshmi, K. S., & Vadivu, G. (2017). Extracting Association Rules from Medical Health Records Using Multi-Criteria Decision Analysis. Procedia Computer Science, 115, 290–295. https://doi.org/10.1016/j.procs.2017.09.137
Le, T., & Vo, B. (2016). The lattice-based approaches for mining association rules: a review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 6(4), 140–151. https://doi.org/10.1002/widm.1181
Lipton, Z. C. (2016). The Mythos of Model Interpretability. In ICML Workshop on Human Interpretability in Machine Learning (WHI 2016) (pp. 1–9).. https://doi.org/10.1016/j.apmr.2007.10.023
Lou, P., Lu, G., Jiang, X., Xiao, Z., Hu, J., & Yan, J. (2021). Cyber intrusion detection through association rule mining on multi-source logs. Applied Intelligence, 51(6), 4043–4057. https://doi.org/10.1007/s10489-020-02007-5
Mahajan, R., Viangteeravat, T., & Akbilgic, O. (2017). Improved detection of congestive heart failure via probabilistic symbolic pattern recognition and heart rate variability metrics. International Journal of Medical Informatics, 108(July), 55–63. https://doi.org/10.1016/j.ijmedinf.2017.09.006
Mori, H., Suzuki, H., Nishihira, K., Honda, S., Kojima, S., Takegami, M., … Yasuda, S. (2020). In-hospital morality associated with acute myocardial infarction was inversely related with the number of coronary risk factors in patients from a Japanese nation-wide real-world database. International Journal of Cardiology: Hypertension, 6(June), 100039. https://doi.org/10.1016/j.ijchy.2020.100039
Mustaqeem, A., Anwar, S. M., Khan, A. R., & Majid, M. (2017). A statistical analysis based recommender model for heart disease patients. International Journal of Medical Informatics, 108(July), 134–145. https://doi.org/10.1016/j.ijmedinf.2017.10.008
Nettleton, D. F. (2013). Data mining of social networks represented as graphs. Computer Science Review, 7(1), 1–34. https://doi.org/10.1016/j.cosrev.2012.12.001
Nguyen, L. T. T., & Nguyen, N. T. (2015). Updating mined class association rules for record insertion. Applied Intelligence, 42(4), 707–721. https://doi.org/10.1007/s10489-014-0614-1
Nourani, V., & Molajou, A. (2017). Application of a hybrid association rules/decision tree model for drought monitoring. Global and Planetary Change, 159, 37–45. https://doi.org/10.1016/j.gloplacha.2017.10.008
Peek, N., Combi, C., Marin, R., & Bellazzi, R. (2015). Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes. Artificial Intelligence in Medicine, 65(1), 61–73. https://doi.org/10.1016/j.artmed.2015.07.003
Prajapati, D. J., Garg, S., & Chauhan, N. C. (2017). MapReduce Based Multilevel Consistent and Inconsistent Association Rule Detection from Big Data Using Interestingness Measures. Big Data Research, 9, 18–27. https://doi.org/10.1016/j.bdr.2017.07.001
Ribas Ripoll, V. J., Wojdel, A., Romero, E., Ramos, P., & Brugada, J. (2016). ECG assessment based on neural networks with pretraining. Applied Soft Computing Journal, 49, 399–406. https://doi.org/10.1016/j.asoc.2016.08.013
Ricci, H., de Araújo, M. N., & Simonetti, S. H. (2016). Early readmission in a high complexity public hospital in cardiology. Revista Da Rede de Enfermagem Do Nordeste, 17(6), 828–834. https://doi.org/10.15253/2175-6783.2016000600014
Rosan, R. P., Farsky, P. S., & Amato, V. L. (2022). Preoperative risk score and in-hospital death following isolated myocardial revascularization surgery Avaliação do risco de óbito intra-hospitalar em cirurgia de revascularização miocárdia isolada Evaluación del riesgo de muerte intrahospitalaria en cirugía de revascularización miocárdica, 2022, 1–18.
Sahar, S. (2003). What Is Interesting: Studies on Interestingness in Knowledge Discovery. Tel-Aviv University.
Shah, R. M., Zhang, Q., Chatterjee, S., Cheema, F., Loor, G., Lemaire, S. A., … Ghanta, R. K. (2018). Incidence, Cost, and Risk Factors for Readmission after Coronary Artery Bypass Grafting. The Annals of Thoracic Surgery, (2019). https://doi.org/10.1016/j.athoracsur.2018.10.077
Shahian, D. M., O’Brien, S. M., Filardo, G., Ferraris, V. A., Haan, C. K., Rich, J. B., … Anderson, R. P. (2009). The Society of Thoracic Surgeons 2008 Cardiac Surgery Risk Models: Part 1-Coronary Artery Bypass Grafting Surgery. Annals of Thoracic Surgery, 88(1 SUPPL.), S2--S22. https://doi.org/10.1016/j.athoracsur.2009.05.053
Tai, Y. M., & Chiu, H. W. (2009). Comorbidity study of ADHD: Applying association rule mining (ARM) to National Health Insurance Database of Taiwan. International Journal of Medical Informatics, 78(12), 75–83. https://doi.org/10.1016/j.ijmedinf.2009.09.005
Valle, M. A., Ruz, G. A., & Morrás, R. (2018). Market basket analysis: Complementing association rules with minimum spanning trees. Expert Systems with Applications, 97, 146–162. https://doi.org/10.1016/j.eswa.2017.12.028
Valverde-Rebaza, J. C., & De Andrade Lopes, A. (2014). Link prediction in online social networks using group information. Universidade de São Paulo. https://doi.org/10.1007/978-3-319-09153-2_3
Vinaya, M., & Shah, K. (2016). Performance Evaluation of Distributed Association Rule Mining Algorithms. Procedia - Procedia Computer Science, 79, 127–134. https://doi.org/10.1016/j.procs.2016.03.017
Weng, C. H. (2016). Identifying association rules of specific later-marketed products. Applied Soft Computing Journal, 38, 518–529. https://doi.org/10.1016/j.asoc.2015.09.047
Yanicelli, L. M., Vegetti, M., Goy, C. B., Martínez, E. C., & Herrera, M. C. (2020). SiTe iC: A telemonitoring system for heart failure patients. International Journal of Medical Informatics, 141(April), 104204. https://doi.org/10.1016/j.ijmedinf.2020.104204
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Dario Brito Calçada; Cantídio de Moura Campos Neto; Vivian Lerner Amato; Roberta Akemi Sinoara ; Solange Oliveira Rezende
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
