Objective Early identification of emergency department (ED) patients who need
hospitalization is essential for quality of care and patient safety. We aimed to compare …

Objective The objective of this study is to apply machine learning algorithms for real-time
and personalized waiting time prediction in emergency departments. We also aim to …

Background and aim: We analyzed an inclusive gradient boosting model to predict hospital
admission from the emergency department (ED) at different time points. We compared its …

Nowadays, data-driven methodologies based on the clinical history of patients represent a
promising research field in which personalized and intelligent healthcare systems can be …

Abstract Machine learning for hospital operations is under-studied. We present a prediction
pipeline that uses live electronic health-records for patients in a UK teaching hospital's …

Length of stay (LOS) and discharge destination predictions are key parts of the discharge
planning process for general medical hospital inpatients. It is possible that machine …

Hospital readmission and length-of-stay predictions provide information on how to manage
hospital bed capacity and the number of required staff, especially during pandemics. We …

Predicting the number of patient visits to hospitals has been acknowledged remarkably
helpful in the decision-making related to allocating limited human and material resources of …

Abstract Background and objective The Accident and Emergency Department (A&ED) is the
frontline for providing emergency care in hospitals. Unfortunately, relative A&ED resources …

In recent years the management of patient flow is one of the main challenges faced by many
hospital establishments, in particular emergency departments (EDs). Increasing number of …