SBBD

Paper Registration

1

Select Book

2

Select Paper

3

Fill in paper information

4

Congratulations

Fill in your paper information

English Information

(*) To change the order drag the item to the new position.

Authors
# Name
1 Márcio de Souza(marciowellsouza@gmail.com)
2 Diego Minatel(dminatel@usp.br)

(*) To change the order drag the item to the new position.

Reference
# Reference
1 Bishara, A., Chiu, C., Whitlock, E. L., Douglas, V. C., Lee, S., Butte, A. J., Leung, J. M., and Donovan, A. L. (2022). Postoperative delirium prediction using machine learning models and preoperative electronic health record data. BMC anesthesiology, 22(1):8.
2 Contreras, M., Kapoor, S., Zhang, J., Davidson, A., Ren, Y., Guan, Z., Ozrazgat-Baslanti, T., Sena, J., Nerella, S., Bihorac, A., et al. (2025). A large language model for delirium prediction in the intensive care unit using structured electronic health records. Scientific Reports, 15(1):38890.
3 Davoudi, A., Ozrazgat-Baslanti, T., Ebadi, A., Bursian, A. C., Bihorac, A., and Rashidi, P. (2017). Delirium prediction using machine learning models on predictive electronic health records data. In 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE), pages 568–573. IEEE.
4 Ely, E. W., Shintani, A., Truman, B., Speroff, T., Gordon, S. M., Harrell Jr, F. E., Inouye, S. K., Bernard, G. R., and Dittus, R. S. (2004). Delirium as a predictor of mortality in mechanically ventilated patients in the intensive care unit. Jama, 291(14):1753–1762.
5 Faceli, K., Lorena, A. C., Gama, J., and Carvalho, A. C. P. d. L. F. d. (2021). Inteligência artificial: uma abordagem de aprendizado de máquina. LTC, Rio de Janeiro, 2nd edition.
6 Inouye, S. K., Westendorp, R. G., and Saczynski, J. S. (2014). Delirium in elderly people. The lancet, 383(9920):911–922.
7 Milbrandt, E. B., Deppen, S., Harrison, P. L., Shintani, A. K., Speroff, T., Stiles, R. A., Truman, B., Bernard, G. R., Dittus, R. S., and Ely, E. W. (2004). Costs associated with delirium in mechanically ventilated patients. Critical care medicine, 32(4):955–962.
8 Pollard, T. J., Johnson, A. E., Raffa, J. D., Celi, L. A., Mark, R. G., and Badawi, O. (2018). The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data, 5(1):1–13.
9 Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN computer science, 2(3):160.
10 United Nations (2019). World population ageing 2019: Highlights.
11 Viegas, A., Von Rekowski, C. P., Araujo, R., Viana-Baptista, M., Macedo, M. P., and Bento, L. (2025). Predicting icu delirium in critically ill covid-19 patients using demographic, clinical, and laboratory admission data: A machine learning approach. Life, 15(7):1045.
12 Witlox, J., Eurelings, L. S., de Jonghe, J. F., Kalisvaart, K. J., Eikelenboom, P., and Van Gool, W. A. (2010). Delirium in elderly patients and the risk of postdischarge mortality, institutionalization, and dementia: a meta-analysis. Jama, 304(4):443–451.
13 Yang, T., Yang, H., Liu, Y., Liu, X., Ding, Y.-J., Li, R., Mao, A.-Q., Huang, Y., Li, X.-L., Zhang, Y., et al. (2024). Postoperative delirium prediction after cardiac surgery using machine learning models. Computers in Biology and Medicine, 169:107818.
14 Zhang, Y., Hu, J., Hua, T., Zhang, J., Zhang, Z., and Yang, M. (2023). Development of a machine learning-based prediction model for sepsis-associated delirium in the intensive care unit. Scientific reports, 13(1):12697.