Machine Learning Models To Predict Length Of Stay In Hospitals

Jain, Raunak; Singh, Mrityunjai; Rao, A. Ravishankar; Garg, Rahul

doi:10.1109/ichi54592.2022.00105

Cited by 3 publications

(1 citation statement)

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“…It is considered by many to be crucial for effective hospital administration, as the allocation of the necessary resources for care is among the first priorities of a hospitalization [Stone et al 2022]. Accurate LOS prediction can, thus, improve the quality of care, reduce cost overhead, and help manage administrative burden [Jain et al 2022, Baniecki et al 2023.…”

Section: Introductionmentioning

confidence: 99%

Predicting Inpatient Admissions in Brazilian Hospitals

Consoli,

Viera,

Bordini

et al. 2024

Anais Do XXIV Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2024)

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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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Section: Introductionmentioning

confidence: 99%