The pressure of ever-increasing patient demand and budget restrictions make hospital bed
management a daily challenge for clinical staff. Most critical is the efficient allocation of …

Abstract Leveraging the Electronic Health Records (EHR) longitudinal data to produce
actionable clinical insights has always been a critical issue for recent studies. Non …

While generative artificial intelligence has gained popularity, eg, for the creation of images, it
can also be used for the creation of synthetic tabular data. This bears great potential …

Pre-training has shown success in different areas of machine learning, such as Computer
Vision, Natural Language Processing (NLP), and medical imaging. However, it has not been …

Background Prediction of length of stay (LOS) at admission time can provide physicians and
nurses insight into the illness severity of patients and aid them in avoiding adverse events …

Efficient bed management minimizes hospital costs and improves efficiency and patient
outcomes. This study presents a predictive hospital-ICU length of stay (LOS) framework at …

This paper focuses on predicting the length of stay for patients on the first day of admission
and propose a predictive model named DGLoS. In order to capture the influence of various …

Hospital overcrowding, driven by both structural management challenges and widespread
medical emergencies, has prompted extensive research into machine learning (ML) …

Forecasting Sepsis length of stay is a challenge for hospitals worldwide. Although there are
many attempts to improve sepsis length of stay prediction; however, there is still lack of …

Length-of-stay prediction has been widely studied as a classification task: will this patient
stay 0-3 days, 3-7 days, or more than 7 days? Yet previous approaches neglect the natural …