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Emergency department (ED) crowding is a global public health issue that has been repeatedly associated with increased mortality. Predicting future service demand would enable preventative measures aiming to eliminate crowding along with…

Emergency department (ED) crowding is a significant threat to patient safety and it has been repeatedly associated with increased mortality. Forecasting future service demand has the potential patient outcomes. Despite active research on…

Machine Learning · Computer Science 2023-09-01 Jalmari Tuominen , Eetu Pulkkinen , Jaakko Peltonen , Juho Kanniainen , Niku Oksala , Ari Palomäki , Antti Roine

Background: Emergency department (ED) overcrowding remains a major challenge, causing delays in care and increased operational strain. Hospital management often reacts to congestion after it occurs. Machine learning predictive modeling…

Machine Learning · Computer Science 2025-04-29 Orhun Vural , Bunyamin Ozaydin , Khalid Y. Aram , James Booth , Brittany F. Lindsey , Abdulaziz Ahmed

Over the past several years, across the globe, there has been an increase in people seeking care in emergency departments (EDs). ED resources, including nurse staffing, are strained by such increases in patient volume. Accurate forecasting…

Environments such as shopping malls, airports, or hospital emergency departments often experience crowding, with many people simultaneously requesting service. Crowding is highly noisy, with sudden overcrowding "spikes". Past research has…

Physics and Society · Physics 2023-08-16 Gil Parnass , Osnat Levtzion-Korach , Renana Peres , Michael Assaf

Overcrowding in emergency departments (ED) remains a persistent operational challenge worldwide, causing delays in care delivery and downstream congestion. ED boarding time, defined as the duration admitted patients remain in the ED while…

Machine Learning · Computer Science 2026-05-20 Orhun Vural , Abdulaziz Ahmed , Ferhat Zengul , James Booth , Bunyamin Ozaydin

There were 25.6 million attendances at Emergency Departments (EDs) in England in 2019 corresponding to an increase of 12 million attendances over the past ten years. The steadily rising demand at EDs creates a constant challenge to provide…

This study presents a deep learning-based framework for predicting emergency department (ED) boarding counts six hours in advance using only operational and contextual data, without patient-level information. Data from ED tracking systems,…

Machine Learning · Computer Science 2025-07-14 Orhun Vural , Bunyamin Ozaydin , James Booth , Brittany F. Lindsey , Abdulaziz Ahmed

Background: The stochastic behavior of patient arrival at an emergency department (ED) complicates the management of an ED. More than 50% of hospitals ED capacity tends to operate beyond its normal capacity and eventually fails to deliver…

Computers and Society · Computer Science 2019-01-10 Avishek Choudhury

The development of respiratory failure is common among patients in intensive care units (ICU). Large data quantities from ICU patient monitoring systems make timely and comprehensive analysis by clinicians difficult but are ideal for…

Machine Learning · Computer Science 2021-05-13 Matthias Hüser , Martin Faltys , Xinrui Lyu , Chris Barber , Stephanie L. Hyland , Tobias M. Merz , Gunnar Rätsch

We study the estimation of the probability distribution of individual patient waiting times in an emergency department (ED). Our feature-rich modelling allows for dynamic updating and refinement of waiting time estimates as patient- and…

Applications · Statistics 2020-06-02 Siddharth Arora , James W. Taylor , Ho-Yin Mak

Emergency department (ED) crowding has been an increasing problem worldwide. Prior research has identified factors that contribute to ED crowding. However, the relationships between these remain incompletely understood. This study's…

Emergency Departments (EDs) are a fundamental element of the Portuguese National Health Service, serving as an entry point for users with diverse and very serious medical problems. Due to the inherent characteristics of the ED; forecasting…

Computers and Society · Computer Science 2023-06-27 Francisco M. Caldas , Cláudia Soares

Emergency Department (ED) overcrowding continues to be a public health issue as well as a patient safety issue. The underlying factors leading to ED crowding are numerous, varied, and complex. Although lack of in-hospital beds is frequently…

Early recognition of clinical deterioration is one of the main steps for reducing inpatient morbidity and mortality. The challenging task of clinical deterioration identification in hospitals lies in the intense daily routines of healthcare…

Urgent care clinics and emergency departments around the world periodically suffer from extended wait times beyond patient expectations due to inadequate staffing levels. These delays have been linked with adverse clinical outcomes.…

Machine Learning · Computer Science 2022-05-27 Paula Maddigan , Teo Susnjak

The intensive care unit (ICU) manages critically ill patients, many of whom face a high risk of mortality. Early and accurate prediction of in-hospital mortality within the first 24 hours of ICU admission is crucial for timely clinical…

Accurately predicting hospital length-of-stay at the time a patient is admitted to hospital may help guide clinical decision making and resource allocation. In this study we aim to build a decision support system that predicts hospital…

Artificial Intelligence · Computer Science 2023-08-08 Mucahit Cevik , Can Kavaklioglu , Fahad Razak , Amol Verma , Ayse Basar

Study Objective: To analyze the factors influencing Emergency Department (ED) overcrowding by examining the impacts of operational, environmental, and external variables, including weather conditions and football games. Methods: This study…

Computers and Society · Computer Science 2025-05-13 Abdulaziz Ahmed , Khalid Y Aram , Mohammed Alzeen , Orhun Vural , James Booth , Brittany F. Lindsey , Bunyamin Ozaydin

Hospitalizations that follow closely on the heels of one or more emergency department visits are often symptoms of missed opportunities to form a proper diagnosis. These diagnostic errors imply a failure to recognize the need for…

Machine Learning · Computer Science 2024-07-02 Dat Hong , Philip M. Polgreen , Alberto Maria Segre
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