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Understanding the use of emerging technologies in the public sector: a review of horizon 2020 projects
The main purpose of this article is to provide an up-to-date understanding of the utilization
and deployment of emerging technologies in the public sector, as this is reflected through 19 …
and deployment of emerging technologies in the public sector, as this is reflected through 19 …
Interpretable and explainable machine learning methods for predictive process monitoring: A systematic literature review
This paper presents a systematic literature review (SLR) on the explainability and
interpretability of machine learning (ML) models within the context of predictive process …
interpretability of machine learning (ML) models within the context of predictive process …
Linked open government data to predict and explain house prices: the case of Scottish statistics portal
Accurately estimating the prices of houses is important for various stakeholders including
house owners, real estate agencies, government agencies, and policy-makers. Towards this …
house owners, real estate agencies, government agencies, and policy-makers. Towards this …
[HTML][HTML] A forecasting approach for hospital bed capacity planning using machine learning and deep learning with application to public hospitals
Abstract Hospital Bed Capacity (HBC) planning affects economic and social sustainability in
healthcare through bed capacity efficiency and medical treatment accessibility …
healthcare through bed capacity efficiency and medical treatment accessibility …
Machine learning methods for predicting the admissions and hospitalisations in the emergency department of a civil and military hospital
Abstract Hospitals' Emergency Departments (ED) have a great relevance in the health of the
population. Properly managing the ED department requires to optimise the service, while …
population. Properly managing the ED department requires to optimise the service, while …
Exploring the quality of dynamic open government data using statistical and machine learning methods
Dynamic data (including environmental, traffic, and sensor data) were recently recognized
as an important part of Open Government Data (OGD). Although these data are of vital …
as an important part of Open Government Data (OGD). Although these data are of vital …
A deep learning architecture for forecasting daily emergency department visits with acuity levels
Abstract Accurate forecasting of Emergency Department (ED) visits is important for decision-
making purposes in hospitals. It helps to form tactical and operational level plans, which …
making purposes in hospitals. It helps to form tactical and operational level plans, which …
[HTML][HTML] Evaluating the impact of exogenous variables for patients forecasting in an emergency department using attention neural networks
Emergency Department overcrowding is a well-known problem. The consequences are long
waiting times for patients, reduced service quality, and the potential for increased mortality …
waiting times for patients, reduced service quality, and the potential for increased mortality …
[HTML][HTML] An explainable machine learning approach for hospital emergency department visits forecasting using continuous training and multi-model regression
Abstract Background and Objective In the last years, the Emergency Department (ED) has
become an important source of admissions for hospitals. Since late 90s, the number of ED …
become an important source of admissions for hospitals. Since late 90s, the number of ED …
[HTML][HTML] An optimized Belief-Rule-Based (BRB) approach to ensure the trustworthiness of interpreted time-series decisions
The accuracy and reliability of XAI methods are important to establish their credibility and
use in complex decision-making tasks. Existing XAI methods provide little information about …
use in complex decision-making tasks. Existing XAI methods provide little information about …