Predicting Inpatient Admissions in Brazilian Hospitals

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


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.


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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:

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