An intriguing vision for transatlantic collaborative health data use and artificial intelligence development

DC Baumgart - NPJ Digital Medicine, 2024 - nature.com
Our traditional approach to diagnosis, prognosis, and treatment, can no longer process and
transform the enormous volume of information into therapeutic success, innovative …

Fault supervision of nuclear research reactor systems using artificial neural networks: A review with results

N Khentout, G Magrotti - Annals of Nuclear Energy, 2023 - Elsevier
On-line condition supervision of nuclear reactor (NR) is of major concern and high-priority
task during operation to ensure safe operation of systems. Usually, faults can occur in …

Machine learning based forecast for the prediction of inpatient bed demand

M Tello, ES Reich, J Puckey, R Maff… - BMC medical informatics …, 2022 - Springer
Background Overcrowding is a serious problem that impacts the ability to provide optimal
level of care in a timely manner. High patient volume is known to increase the boarding time …

[HTML][HTML] A forecasting approach for hospital bed capacity planning using machine learning and deep learning with application to public hospitals

Y Mahmoudian, A Nemati, AS Safaei - Healthcare Analytics, 2023 - Elsevier
Abstract Hospital Bed Capacity (HBC) planning affects economic and social sustainability in
healthcare through bed capacity efficiency and medical treatment accessibility …

A machine learning solution for bed occupancy issue for smart healthcare sector

S Gochhait, SA Butt, E De-La-Hoz-Franco… - Automatic Control and …, 2021 - Springer
The health care domain is a culmination and emergence of many other economic sectors
that give different services from patient treatment to healing, protective, rehabilitation, and …

Multi-objective reinforcement learning based healthcare expansion planning considering pandemic events

SS Shuvo, H Symum, MR Ahmed… - IEEE Journal of …, 2022 - ieeexplore.ieee.org
Hospital capacity expansion planning is critical for a healthcare authority, especially in
regions with a growing diverse population. Policymaking to this end often requires satisfying …

Characterizing and Improving the Robustness of Predict-Then-Optimize Frameworks

S Johnson-Yu, J Finocchiaro, K Wang… - … Conference on Decision …, 2023 - Springer
Optimization tasks situated in incomplete information settings are often preceded by a
prediction problem to estimate the missing information; past work shows the traditional …

[HTML][HTML] Towards reliable forecasting of healthcare capacity needs: A scoping review and evidence mapping

S Grøntved, MJ Kirkeby, SP Johnsen, J Mainz… - International Journal of …, 2024 - Elsevier
Background The COVID-19 pandemic has highlighted the critical importance of robust
healthcare capacity planning and preparedness for emerging crises. However, healthcare …

[HTML][HTML] Forecasting Hospital Room and Ward Occupancy Using Static and Dynamic Information Concurrently: Retrospective Single-Center Cohort Study

H Seo, I Ahn, H Gwon, H Kang, Y Kim… - JMIR Medical …, 2024 - medinform.jmir.org
Background Predicting the bed occupancy rate (BOR) is essential for efficient hospital
resource management, long-term budget planning, and patient care planning. Although …

Artificial intelligence forecasting census and supporting early decisions

TE Griner, M Thompson, H High… - Nursing Administration …, 2020 - journals.lww.com
Matching resources to demand is a daily challenge for hospital leadership. In
interdisciplinary collaboration, nurse leaders and data scientists collaborated to develop …