Predicting Inpatient Admissions in Brazilian Hospitals

  • Bernardo Consoli PUCRS
  • Renata Viera University of Évora
  • Rafael H. Bordini PUCRS
  • Isabel H. Manssour PUCRS

Resumo


Patient length-of-stay prediction is a topic of interest for hospital administrators, as it can aid in planning and the allocation of critical resources. Ideal resource allocation can result in better care and reduced costs. Artificial Intelligence solutions have been tested for this purpose using several datasets for both foreign and Brazilian hospitals, but focusing on long-term inpatient care or Intensive Care Unit patient flow. We propose using similar solutions to predict inpatient flow from common patient entry points, such as emergency care or walk-in appointments, in an effort to better understand whether a patient will require outpatient care or inpatient admission as early as possible. Our solution was able to predict inpatient flow with as much as 88% accuracy.

Referências

Alghatani, K., Ammar, N., Rezgui, A., and Shaban-Nejad, A. (2021). Predicting intensive care unit length of stay and mortality using patient vital signs: Machine learning model development and validation. JMIR Med Inform, 9(5):e21347.

Baniecki, H., Sobieski, B., Bombiński, P., Szatkowski, P., and Biecek, P. (2023). Hospital length of stay prediction based on multi-modal data towards trustworthy human-ai collaboration in radiomics. In Juarez, J. M., Marcos, M., Stiglic, G., and Tucker, A., editors, Artificial Intelligence in Medicine, pages 65–74, Cham. Springer Nature Switzerland.

Bertsimas, D., Pauphilet, J., Stevens, J., and Tandon, M. (2020). Predicting inpatient flow at a major hospital using interpretable analytics. Preprint at [link].

Bradley, A. P. (1997). The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern recognition, 30(7):1145–1159.

Brink, A., Alsma, J., van Attekum, L. A., Bramer, W. M., Zietse, R., Lingsma, H., and Schuit, S. C. (2022). Predicting inhospital admission at the emergency department: a systematic review. Emergency Medicine Journal, 39(3):191–198.

Carter, J. V., Pan, J., Rai, S. N., and Galandiuk, S. (2016). Roc-ing along: Evaluation and interpretation of receiver operating characteristic curves. Surgery, 159(6):1638–1645.

Chen, T. and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In In 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Dining, pages 785–794, San Francisco (CA), ACM, pages 785–794.

Consoli, B. S., dos Santos, H. D. P., Ulbrich, A. H. D., Vieira, R., and Bordini, R. H. (2022). Brateca (brazilian tertiary care dataset): a clinical information dataset for the portuguese language. In Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022), Marseille, 20-25 June, 2022, França.

Consoli, B. S., Vieira, R., and Bordini, R. H. (2023). Benchmarking the brateca clinical data collection for prediction tasks. In HEALTHINF, pages 338–345.

Cusido, J., Comalrena, J., Alavi, H., and Llunas, L. (2022). Predicting hospital admissions to reduce crowding in the emergency departments. Applied Sciences, 12:10764.

da Saúde, M. (1987). TERMINOLOGIA BÁSICA EM SAÚDE. Secretaria Nacional de Organização e Desenvolvimento de Serviços de Saúde, [link], page 23.

Feliciana Silva, F., Macedo da Silva Bonfante, G., Reis, I. A., André da Rocha, H., Pereira Lana, A., and Leal Cherchiglia, M. (2020). Hospitalizations and length of stay of cancer patients: A cohort study in the brazilian public health system. PLOS ONE, 15(5):1–13.

Hong, W. S., Haimovich, A. D., and Taylor, R. A. (2018). Predicting hospital admission at emergency department triage using machine learning. PLOS ONE, 13(7):1–13.

Jain, R., Singh, M., Rao, A. R., and Garg, R. (2022). Machine learning models to predict length of stay in hospitals. In 2022 IEEE 10th International Conference on Healthcare Informatics (ICHI), pages 545–546.

Jaotombo, F., Pauly, V., Fond, G., Orleans, V., Auquier, P., Ghattas, B., and Boyer, L. (2023). Machine-learning prediction for hospital length of stay using a french medico-administrative database. Journal of Market Access & Health Policy, 11(1):2149318. PMID: 36457821.

Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., , Celi, L. A., and Mark, R. G. (2016). Mimic-iii, a freely accessible critical care database. Scientific Data 3, 160035.

Kadri, F., Dairi, A., Harrou, F., and Sun, Y. (2023). Towards accurate prediction of patient length of stay at emergency department: a gan-driven deep learning framework. Journal of Ambient Intelligence and Humanized Computing, Feb 3:1-15.

Knevel, R. and Liao, K. P. (2023). From real-world electronic health record data to real-world results using artificial intelligence. Annals of the Rheumatic Diseases, 82(3):306–311.

Kurtz, P., Peres, I., Soares, M., Soares, M., Salluh, J. I. F., and Bozza, F. A. (2022). Hospital length of stay and 30-day mortality prediction in stroke: A machine learning analysis of 17,000 icu admissions in brazil. Neurocritical Care, 37(2):313–321.

Natália Boff Medeiros, Flávio Sanson Fogliatto, M. K. R. and Tortorella, G. L. (2023). Predicting the length-of-stay of pediatric patients using machine learning algorithms. International Journal of Production Research, 0(0):1–14.

Peres, I. T., Hamacher, S., Cyrino Oliveira, F. L., Bozza, F. A., and Salluh, J. I. F. (2022). Data-driven methodology to predict the icu length of stay: A multicentre study of 99,492 admissions in 109 brazilian units. Anaesthesia Critical Care & Pain Medicine, 41(6):101142.

Rajkomar, A., Oren, E., Chen, K., ai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., Sundberg, P., Yee, H., Zhang, K., Zhang, Y., Flores, G., Duggan, G. E., Irvine, J., Le, Q., Litsch, K., Mossin, A., Tansuwan, J., Wang, D., Wexler, J., Wilson, J., Ludwig, D., Volchenboum, S. L., Chou, K., Pearson, M., Madabushi, S., Shah, N. H., Butte, A. J., Howell, M. D., Cui, C., Corrado, G. S., and Dean, J. (2018). Scalable and accurate deep learning with electronic health records. Nature Digital Medicine 1, 18.

Sparck Jones, K. (1972). A statistical interpretation of term specificity and its application in retrieval. Journal of documentation, 28(1):11–21.

Stone, K., Zwiggelaar, R., Jones, P., and Mac Parthaláin, N. (2022). A systematic review of the prediction of hospital length of stay: Towards a unified framework. PLOS Digital Health, 1(4):1–38.

Yang, S., Varghese, P., Stephenson, E., Tu, K., and Gronsbell, J. (2022). Machine learning approaches for electronic health records phenotyping: a methodical review. Journal of the American Medical Informatics Association, 30(2):367–381.
Publicado
25/06/2024
CONSOLI, Bernardo; VIERA, Renata; BORDINI, Rafael H.; MANSSOUR, Isabel H.. Predicting Inpatient Admissions in Brazilian Hospitals. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 24. , 2024, Goiânia/GO. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2024 . p. 284-295. ISSN 2763-8952. DOI: https://doi.org/10.5753/sbcas.2024.2192.

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