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Authors
# Name
1 Douglas Tranquilino(jdts1@discente.ifpe.edu.br)
2 Marcos Vinicius(mvvm@discente.ifpe.edu.br)
3 Rafael de Carli(rafael.carli@upe.br)
4 Gustavo Callou(gustavo.callou@ufrpe.br)
5 Eduardo Tavares(eagt@cin.ufpe.br)
6 Thiago Bezerra(thiago.bezerra@palmares.ifpe.edu.br)

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Reference
# Reference
1 Abujaber, A., Yaseen, S., Imam, Y., Nashwan, A., and Akhtar, N. (2024). Machine learning-based prediction of one-year mortality in ischemic stroke patients. Oxford Open Neuroscience, 3:kvae011
2 Bezerra, T., Vinicius, M., Ciane, A., Callou, G., França, C., and Tavares, E. (2025). An approach based on iot and machine learning for monitoring patients on healthcare centers. In Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS), pages 260–271. SBC.
3 Breiman, L. (2001). Random forests. Machine learning, 45:5–32.
4 Chen, T. and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pages 785–794
5 Desai, A., Oh, D., Rao, E. M., Sahoo, S., Mahajan, U. V., Labak, C. M., Mauria, R., Shah, V. S., Nguyen, Q., Herring, E. Z., et al. (2023). Impact of anemia on acute ischemic stroke outcomes: a systematic review of the literature. PLoS One, 18(1):e0280025
6 dos Santos, J. V., dos Santos Leopoldino, D. d. J., Silva, A. B. B., Lima, A. C. G., Teshima, I. E. N. S., de Oliveira Neto, E. B., Milones, M. E. d. S. V., de Santa Maria, K. C., Bomfim, L. C., de Albuquerque Maranhão, E. B., et al. (2025). Acidente vascular cerebral no brasil: aspectos epidemiológicos da mortalidade no período de 2019 a 2023. Brazilian Journal of Implantology and Health Sciences, 7(3):1429–1439
7 Hemmati, D., Eissazade, N., Eghdami, S., Mirzaasgari, Z., and Amouzegar, A. (2025). Three-month functional outcomes of acute ischemic stroke in patients with chronic renal function impairment. PLoS One, 20(5):e0323995
8 Huang, J., Chen, H., Deng, J., Liu, X., Shu, T., Yin, C., Duan, M., Fu, L., Wang, K., and Zeng, S. (2023a). Interpretable machine learning for predicting 28-day all-cause in-hospital mortality for hypertensive ischemic or hemorrhagic stroke patients in the icu: a multi-center retrospective cohort study with internal and external cross-validation. Frontiers in Neurology, 14:1185447
9 Huang, R., Liu, J., Wan, T. K., Siriwanna, D., Woo, Y. M. P., Vodencarevic, A., Wong, C. W., and Chan, K. H. K. (2023b). Stroke mortality prediction based on ensemble learning and the combination of structured and textual data. Computers in Biology and Medicine, 155:106176
10 Jiang, Z., Wang, K., Duan, H., Du, H., Gao, S., Chen, J., and Fang, S. (2024). Association between stress hyperglycemia ratio and prognosis in acute ischemic stroke: a systematic review and meta-analysis. BMC neurology, 24(1):13
11 Johnson, A., Bulgarelli, L., Pollard, T., Horng, S., Celi, L. A., and Mark IV, R. (2021). Mimic-iv (version 1.0). physionet. 2021. DOI: https://doi. org/10.13026/s6n6-xd98
12 Mitsios, J. P., Ekinci, E. I., Mitsios, G. P., Churilov, L., and Thijs, V. (2018). Relationship between glycated hemoglobin and stroke risk: a systematic review and meta-analysis. Journal of the American Heart Association, 7(11):e007858
13 Pelouto, A., Reimer, J., Hoorn, E. J., Zandbergen, A. A., and den Hertog, H. M. (2024). Hyponatremia is associated with unfavorable outcomes after reperfusion treatment in acute ischemic stroke. European Journal of Neurology, 31(3):e16156
14 Tam, C. W., Shum, H.-P., and Yan, W. (2019). Impact of dysnatremia and dyskalemia on prognosis in patients with aneurysmal subarachnoid hemorrhage: a retrospective study. Indian Journal of Critical Care Medicine: Peer-reviewed, Official Publication of Indian Society of Critical Care Medicine, 23(12):562
15 Wu, Y. and Fang, Y. (2020). Stroke prediction with machine learning methods among older chinese. International journal of environmental research and public health, 17(6):1828
16 Zhang, H., Yue, K., Jiang, Z., Wu, X., Li, X., Luo, P., and Jiang, X. (2023). Incidence of stress-induced hyperglycemia in acute ischemic stroke: a systematic review and meta-analysis. Brain Sciences, 13(4):556
17 Zheng, Y., Guo, Z., Zhang, Y., Shang, J., Yu, L., Fu, P., Liu, Y., Li, X., Wang, H., Ren, L., et al. (2022). Rapid triage for ischemic stroke: a machine learning-driven approach in the context of predictive, preventive and personalised medicine. EPMA journal, 13(2):285–298
18 Zhu, E., Chen, Z., Ai, P., Wang, J., Zhu, M., Xu, Z., Liu, J., and Ai, Z. (2023). Analyzing and predicting the risk of death in stroke patients using machine learning. Frontiers in Neurology, 14:1096153