[HTML][HTML] Comparative study of machine learning methods for COVID-19 transmission forecasting
Within the recent pandemic, scientists and clinicians are engaged in seeking new
technology to stop or slow down the COVID-19 pandemic. The benefit of machine learning …
technology to stop or slow down the COVID-19 pandemic. The benefit of machine learning …
A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models
This study aims to develop an assumption-free data-driven model to accurately forecast
COVID-19 spread. Towards this end, we firstly employed Bayesian optimization to tune the …
COVID-19 spread. Towards this end, we firstly employed Bayesian optimization to tune the …
Predicting hospital emergency department visits accurately: A systematic review
E Silva, MF Pereira, JT Vieira… - … Journal of Health …, 2023 - Wiley Online Library
Objectives The emergency department (ED) is a very important healthcare entrance point,
known for its challenging organisation and management due to demand unpredictability. An …
known for its challenging organisation and management due to demand unpredictability. An …
Unlocking the potential of wastewater treatment: Machine learning based energy consumption prediction
Wastewater treatment plants (WWTPs) are energy-intensive facilities that fulfill stringent
effluent quality norms. Energy consumption prediction in WWTPs is crucial for cost savings …
effluent quality norms. Energy consumption prediction in WWTPs is crucial for cost savings …
[HTML][HTML] A systematic review of the modelling of patient arrivals in emergency departments
S Jiang, Q Liu, B Ding - Quantitative Imaging in Medicine and …, 2023 - ncbi.nlm.nih.gov
Background Accident and Emergency Department (AED) is the frontline of providing
emergency care in a hospital and research focusing on improving decision-makings and …
emergency care in a hospital and research focusing on improving decision-makings and …
Towards accurate prediction of patient length of stay at emergency department: a GAN-driven deep learning framework
Recently, the hospital systems face a high influx of patients generated by several events,
such as seasonal flows or health crises related to epidemics (eg, COVID'19). Despite the …
such as seasonal flows or health crises related to epidemics (eg, COVID'19). Despite the …
Forecasting and explaining emergency department visits in a public hospital
S Petsis, A Karamanou, E Kalampokis… - Journal of Intelligent …, 2022 - Springer
Abstract Emergency Departments (EDs) are the most overcrowded places in public
hospitals. Machine learning can support decisions on effective ED resource management by …
hospitals. Machine learning can support decisions on effective ED resource management by …
A deep learning based hybrid architecture for weekly dengue incidences forecasting
Dengue is a mosquito-borne viral disease widely spread in tropical and subtropical regions.
Its adverse impact on the human health and global economies cannot be overstated. In …
Its adverse impact on the human health and global economies cannot be overstated. In …
Deep generative learning-based 1-SVM detectors for unsupervised COVID-19 infection detection using blood tests
A sample blood test has recently become an important tool to help identify false-
positive/false-negative real-time reverse transcription polymerase chain reaction (rRT-PCR) …
positive/false-negative real-time reverse transcription polymerase chain reaction (rRT-PCR) …
Predicting hospital emergency department visits with deep learning approaches
Overcrowding in emergency department (ED) causes lengthy waiting times, reduces
adequate emergency care and increases rate of mortality. Accurate prediction of daily ED …
adequate emergency care and increases rate of mortality. Accurate prediction of daily ED …