Understanding the use of emerging technologies in the public sector: a review of horizon 2020 projects

E Kalampokis, N Karacapilidis, D Tsakalidis… - … : Research and Practice, 2023‏ - dl.acm.org
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 …

Interpretable and explainable machine learning methods for predictive process monitoring: A systematic literature review

N Mehdiyev, M Majlatow, P Fettke - arxiv preprint arxiv:2312.17584, 2023‏ - arxiv.org
This paper presents a systematic literature review (SLR) on the explainability and
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

A Karamanou, E Kalampokis, K Tarabanis - Big Data Research, 2022‏ - Elsevier
Accurately estimating the prices of houses is important for various stakeholders including
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

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 …

Machine learning methods for predicting the admissions and hospitalisations in the emergency department of a civil and military hospital

H Álvarez-Chaves, P Muñoz, MD R-Moreno - Journal of Intelligent …, 2023‏ - Springer
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 …

Exploring the quality of dynamic open government data using statistical and machine learning methods

A Karamanou, P Brimos, E Kalampokis, K Tarabanis - Sensors, 2022‏ - mdpi.com
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 …

A deep learning architecture for forecasting daily emergency department visits with acuity levels

X Zhao, K Li, CKE Ang, AFW Ho, N Liu, MEH Ong… - Chaos, Solitons & …, 2022‏ - Elsevier
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 …

[HTML][HTML] Evaluating the impact of exogenous variables for patients forecasting in an emergency department using attention neural networks

H Álvarez-Chaves, I Maseda-Zurdo, P Muñoz… - Expert Systems with …, 2024‏ - Elsevier
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 …

[HTML][HTML] An explainable machine learning approach for hospital emergency department visits forecasting using continuous training and multi-model regression

C Peláez-Rodríguez, R Torres-López… - Computer Methods and …, 2024‏ - Elsevier
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 …

[HTML][HTML] An optimized Belief-Rule-Based (BRB) approach to ensure the trustworthiness of interpreted time-series decisions

SF Nimmy, OK Hussain, RK Chakrabortty… - Knowledge-Based …, 2023‏ - Elsevier
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 …