Mahmoud Elbattah

• University of Picardie Jules Verne

Healthcare systems are increasingly challenged by the phenomenal growth of population ageing. Healthcare executives are, and will be, in an inevitable need of evidence-based artifacts for decision making. The paper addresses issues in the context of discharge planning for elderly patients with application to hip fracture care in Ireland. A hybrid a...

Background: Overcrowded emergency departments (EDs) create significant challenges for patient management and hospital efficiency. In response, Amiens Picardy University Hospital (APUH) developed the “Prediction of the Patient Pathway in the Emergency Department” (3P-U) model to enhance patient flow management. Objectives: To develop and clinically...

Special Session: AI Innovations in Autism Diagnosis https://aiih.cc/autism-diagnosis/ Within the 2nd International Conference on Artificial Intelligence in Healthcare (AIiH 2025) 8-10 September, Jesus College, University of Cambridge, UK (Remote Participation Available) https://aiih.cc/ Submission Deadline: 11th April 2025

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterised by challenges in social communication, repetitive behaviours, and restricted interests [1]. Early and accurate diagnosis is critical for effective intervention, enabling individuals with ASD to achieve better developmental outcomes and an improved quality of life...

Large Language Models (LLMs) hold promise for advancing Emergency Medicine by enhancing operational efficiency and supporting decision-making. This scoping review explores the ethical, legal, and global considerations influencing LLM deployment in emergency care. Key ethical concerns, including patient safety, data privacy, and transparency, emphas...

Despite the significant advances made by Artificial Intelligence (AI) models in enhancing medical diagnostics and prognostics, their opacity poses a hurdle to widespread clinical adoption. In this regard, Explainable AI (XAI) aims to demystify these complex models, such as neural networks, by revealing the reasoning behind predictions. However, a n...

Explainable Artificial Intelligence (XAI) has the potential to revolutionize healthcare by providing more transparent, trustworthy, and understandable predictions made by AI models. To this end, the present study aims to develop an explainable NLP model for predicting patient admissions to the emergency department based on triage notes. We utilize...

Autism spectrum disorder (ASD) is a lifelong neuro-developmental disorder that is generally marked by a set of communication and social impairments. The early diagnosis of autism is genuinely beneficial for the welfare of children and parents as well. However, making an accurate diagnosis of autism remains a challenging task, which requires an inte...

In the field of emergency medicine (EM), the use of decision support tools based on artificial intelligence has increased markedly in recent years. In some cases, data are omitted deliberately and thus constitute “data not purposely collected” (DNPC). This accepted information bias can be managed in various ways: dropping patients with missing data...

Autism spectrum disorder (ASD) is a lifelong neuro-developmental disorder that is generally marked by a set of communication and social impairments. The early diagnosis of autism is genuinely beneficial for the welfare of children and parents as well. However, making an accurate diagnosis of autism remains a challenging task, which requires an inte...

The availability of data is a key enabler for researchers across different disciplines. However, domains, such as healthcare, are still fundamentally challenged by the paucity and imbalance of datasets. Health data could be inaccessible due to a variety of hurdles such as privacy concerns, or lack of sharing incentives. In this regard, this study a...

Background: During the coronavirus disease 2019 (COVID-19) pandemic, calculation of the number of emergency department (ED) beds required for patients with vs. without suspected COVID-19 represented a real public health problem. In France, Amiens Picardy University Hospital (APUH) developed an Artificial Intelligence (AI) project called "Predictio...

The availability of data is a key enabler for researchers across different disciplines. However, domains, such as healthcare, are still fundamentally challenged by the paucity and imbalance of datasets. Health data could be inaccessible due to a variety of hurdles such as privacy concerns, or lack of sharing incentives. In this regard, this study a...

The potentials of Transfer Learning (TL) have been well-researched in areas such as Computer Vision and Natural Language Processing. This study aims to explore a novel application of TL to detect Autism Spectrum Disorder. We seek to develop an approach that combines TL and eye-tracking, which is commonly used for analyzing autistic features. The ke...

The advent of transformer models has allowed for tremendous progress in the Natural Language Processing (NLP) domain. Pretrained transformers could successfully deliver the state-of-the-art performance in a myriad of NLP tasks. This study presents an application of transformers to learn contextual embeddings from free-text triage notes, widely reco...

Background The early diagnosis of autism spectrum disorder (ASD) is highly desirable but remains a challenging task, which requires a set of cognitive tests and hours of clinical examinations. In addition, variations of such symptoms exist, which can make the identification of ASD even more difficult. Although diagnosis tests are largely developed...

Data Analytics is rapidly expanding within the healthcare domain to help develop strategies for improving the quality of care and curbing costs as well. Natural Language Processing (NLP) solutions have received particular attention whereas a large part of clinical data is stockpiled into unstructured physician or nursing notes. In this respect, we...

9th IEEE International Conference on Healthcare Informatics (IEEE ICHI), ELECTR NETWORK, AUG 09-12, 2021

Over the past decade, deep learning has achieved unprecedented successes in a diversity of application domains, given large-scale datasets. However, particular domains, such as healthcare, inherently suffer from data paucity and imbalance. Moreover, datasets could be largely inaccessible due to privacy concerns, or lack of data-sharing incentives....

BACKGROUND The early diagnosis of autism spectrum disorder (ASD) is highly desirable but remains a challenging task, which requires a set of cognitive tests and hours of clinical examinations. In addition, variations of such symptoms exist, which can make the identification of ASD even more difficult. Although diagnosis tests are largely developed...

The implementation of Data Analytics has achieved a significant momentum across a very wide range of domains. Part of that progress is directly linked to the implementation of Text Analytics solutions. Organisations increasingly seek to harness the power of Text Analytics to automate the process of gleaning insights from unstructured textual data....

Overcrowding in Emergency Departments (ED) is considered as an international issue, which could have adverse impacts on multiple care outcomes such as the length of stay for example. Part of the solution could lie in the early prediction of the patient outcome as discharge or hospitalization. This study applies Deep Learning to this end. A large-sc...

Autism Spectrum Disorder (ASD) is a lifelong condition generally characterized by social and communication impairments. The early diagnosis of ASD is highly desirable, yet it could be complicated by several factors. Standard tests typically require intensive efforts and experience, which calls for developing assistive tools. In this respect, this s...

This study explores a Machine Learning-based approach for generating synthetic eye-tracking data. In this respect, a novel application of Recurrent Neural Networks is experimented. Our approach is based on learning the sequence patterns of eye-tracking data. The key idea is to represent eye-tracking records as textual strings, which describe the se...

The need for explainable AI is becoming increasingly important for critical decision domains such as healthcare for example. In this context, this paper is concerned with explaining the predictions of Convolutional Neural Networks (CNNs) with particular focus on multivariate Time Series (TS) problems. The approach is based on heatmaps as a visual m...

The healthcare arena has been undergoing impressive transformations thanks to advances in the capacity to capture, store, process, and learn from data. This paper re-visits the problem of predicting the risk of in-hospital mortality based on Time Series (TS) records emanating from ICU monitoring devices. The problem basically represents an applicat...

Data analytics has opened the door for improving many aspects pertaining to the delivery of healthcare. This study avails of unsupervised machine learning to extract knowledge from the Irish hip fracture database (IHFD). The dataset under consideration contained patient records over three years 2013–2015. The process of knowledge discovery included...

The use of Machine Learning (ML) has achieved a significant momentum across a very wide range of domains. This paper aims to provide a meeting point for discussing the integration of Modeling and Simulation (M&S) with ML. The discussion presents arguments in favour of why and how the M&S practice can avail of ML in different modalities. In this con...

Autism spectrum disorder (ASD) is a lifelong condition characterized by social and communication impairments. This study attempts to apply unsupervised Machine Learning to discover clusters in ASD. The key idea is to learn clusters based on the visual representation of eye-tracking scanpaths. The clustering model was trained using compressed repres...

Autism spectrum disorder (ASD) is a lifelong condition generally characterized by social and communication impairments. The early diagnosis of ASD is highly desirable, and there is a need for developing assistive tools to support the diagnosis process in this regard. This paper presents an approach to help with the ASD diagnosis with a particular f...

The current trend towards the Semantic Web and Linked Data has resulted in an unprecedented volume of data being continuously published on the Linked Open Data (LOD) cloud. Massive Knowledge Graphs (KGs) are increasingly constructed and enriched based on large amounts of unstructured data. However, the data quality of KGs can still suffer from a va...

The development of care pathways is increasingly becoming an instrumental artefact towards improving the quality of care and cutting costs. This paper presents a framework that incorporates Simulation Modeling along with Machine Learning (ML) for the purpose of designing pathways and evaluating the return on investment of implementation. The study...

Recent trends towards data-driven methods may require a substantial rethinking regarding the practice of Modeling &Simulation (M&S). Machine Learning (ML) is now becoming an instrumental artefact for developing new insights, or improving already established knowledge. Reflecting this broad scope, the paper presents a conceptual framework to guide t...

Recent trends towards data-driven methods may require a substantial rethinking of the process of developing simulation models. In this respect, the chapter aims to promote a discussion on the opportunities and potential for integrating simulation modeling with Machine Learning (ML) in particular. To provide a practical perspective, a realistic use...

Recent trends towards data-driven methods may require a substantial rethinking of the process of developing simulation models. For instance, Machine Learning (ML) has demonstrated great potentials for constructing new knowledge, or improving already established knowledge. Reflecting this trend, the study lends support to the discussion of why and h...

Machine Learning (ML) has demonstrated great potentials for constructing new knowledge, or improving already established knowledge. Reflecting this trend, the paper lends support to the discussion of why and how should ML support the practice of modeling and simulation? Subsequently, the study goes through a use case in relation to healthcare, whic...

The world of Big Data continues to expand, and forge the landscape of data analytics. Datasets are rapidly growing in size and complexity, and there is a pressing need to develop solutions that can harness this deluge of data to produce useful insights. Unsupervised techniques, such as clustering, present as appropriate methods for exploring and su...

The dataset contains more than 21M hierarchical relationships about ≈10M topics extracted from Freebase knowledgebase. The topics span the various categories of Freebase including Science & Technology, Arts & Entertainment, Sports, Society, Products & Services, Transportation, Time & Space, Special Interests, and Commons. The relationships describe...

Linked Data has gained a significant momentum over the last years through various initiatives embraced by academia and industry as well. In this paper, we introduce ScholarBase, a research project in progress that is aimed to serve as a Linked Data repository for cross-domain scholarly data. ScholarBase can be conceived as a knowledgebase that weav...

Predictive analytics have proved promising capabilities and opportunities to many aspects of healthcare practice. Data-driven insights can provide an important part of the solution for curbing rising costs and improving care quality. The paper implements machine learning techniques in an attempt to support decision making in relation to elderly hea...

Machine learning continues to forge the future of decision making in a broad diversity of domains including healthcare. Data-driven methods are increasingly geared towards leveraging evidence-based insights from large volumes of patient data. In this context, this paper embraces a mere data-driven approach for the segmentation of patients with appl...

Faced with the challenge of population ageing, healthcare providers are increasingly in need for evidence-based artefacts to support the decision making process. In this regard, the paper avails of machine learning techniques in a bid to support the elderly care planning with application to hip fracture care in Ireland. Specifically, the inpatient...

For the foreseeable future, quality improvement of Healthcare Service Systems (HSS) will depend on implementing a health information infrastructure that supports human decision making about protocols, processes, and procedures that work together to support the valuebased paradigm. Based on a recent formalization of pathways-based coordination of ca...

Population ageing is increasing in a rapid pace worldwide, and especially within developed countries. Extraordinary economic challenges are therefore in prospect with regard to healthcare delivery. In this respect, healthcare executives increasingly need tools that can accurately assess the impacts of the foreseen demographic transition. The paper...

The practice of simulation modeling has been largely dominated by assumptions based on the modeler’s knowledge and preferences, available information, or other factors, which are generally approximate and embedded within the model’s uncertainty. In this study, we present an approach that aims to assist simulation modeling with data-driven knowledge...

Visualisation is attaining a growing recognition as a pivotal part of the data analysis process. Visualisation-based solutions are increasingly used to adequately explore and communicate understanding of large-scale datasets. This paper presents a web-based visualisation tool, named FreebaseViz, for visually exploring the schema of Freebase. The vi...

Ontology has been increasingly recognised as an instrumental artifact to help make sense of large amounts of data. However, the challenges of Big Data significantly overburden the process of ontology storage and query particularly. In this respect, the paper aims to convey considerations in relation to improving the practice of storing or querying...

Clinical pathways (CPs) have been increasingly recognised as an instrumental evidence-based artifact that can support clinical decision making and care planning. However, research focusing on modeling and simulation of CPs is still sparse, despite significant individual endeavours. Initially, the paper conducts a systematic literature review with t...

Freebase is intended to be an important component of the Linked Open Data (LOD). The paper presents a graph-driven methodology for the analysis and visualisation of Freebase complex schema. First, the methodology utilises Freebase schema types, "Included Type" relationships and "Instance Count" properties to construct a directed weighted graph sche...

Big Data is a rapidly evolving and maturing field which places significant data storage and processing power at our disposal. To take advantage of this power, we need to create new means of collecting and processing large volumes of data at high speed. Meanwhile, as companies and organizations, such as health services, realize the importance and va...

Freebase can be considered as one of the largest sources in the Linked Open Data (LOD) cloud. The paper adopts a graph-driven approach to analyse and visualise Freebase schema. Firstly, the paper demonstrates the extraction process of Freebase schema through stepwise procedures that maintain the schema structure. Secondly, the extracted types and r...

Successful supply chains management has become a key factor for enterprises to achieve and maintain their competitive advantage. The increasing complexity and agility of supply chains are sustainably growing challenges. Simulation provides advantages over traditional analytical methods in planning and optimisation of supply chains. This paper prese...

The popularity of enterprise resource planning is globally increasing for being the magical word to integrate and automate all areas of business operations. However, many organizations are still having hard times to select the proper ERP package. Selection of an ERP system is a crucial decision since implementing ERP is complex, very expensive and...

Presenting a diversity of criteria that can be used to guide the selection of ERP systems. The criteria covers seven main groups including: i) Cost-Related, ii) Implementation Time, iii) Vendor-Related, iv) User-Related, v) Technology-Related, vi) System-Related, and vii)Organizational Requirements. Original Paper: Hegazy, A. E. F. A., ElBattah, M...