Outputs (11)

Exploring the ethical challenges of large language models in emergency medicine: A comparative international review (2025)
Presentation / Conference Contribution

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 em... Read More about Exploring the ethical challenges of large language models in emergency medicine: A comparative international review.

Editorial: Improving autism spectrum disorder diagnosis using machine learning techniques (2024)
Journal Article

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by challenges in social communication, repetitive behaviors, and restricted interests (American Psychiatric Association, 2013). Early and accurate diagnosis is cri... Read More about Editorial: Improving autism spectrum disorder diagnosis using machine learning techniques.

Consistency of XAI models against medical expertise: An assessment protocol (2024)
Presentation / Conference Contribution

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... Read More about Consistency of XAI models against medical expertise: An assessment protocol.

Explainable NLP model for predicting patient admissions at emergency department using triage notes (2024)
Presentation / Conference Contribution

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 mo... Read More about Explainable NLP model for predicting patient admissions at emergency department using triage notes.

How can machine learning support the practice of modeling and simulation? —A review and directions for future research (2020)
Presentation / Conference Contribution

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 argu... Read More about How can machine learning support the practice of modeling and simulation? —A review and directions for future research.

ML-Aided simulation: A conceptual framework for integrating simulation models with machine learning (2018)
Presentation / Conference Contribution

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 esta... Read More about ML-Aided simulation: A conceptual framework for integrating simulation models with machine learning.

ScholarBase: Towards a cross-domain knowledgebase for linked scholarly data (2017)
Presentation / Conference Contribution

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 r... Read More about ScholarBase: Towards a cross-domain knowledgebase for linked scholarly data.

Data-driven patient segmentation using K-means clustering: The case of hip fracture care in Ireland (2017)
Presentation / Conference Contribution

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... Read More about Data-driven patient segmentation using K-means clustering: The case of hip fracture care in Ireland.

Coupling simulation with machine learning: A hybrid approach for elderly discharge planning (2016)
Presentation / Conference Contribution

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... Read More about Coupling simulation with machine learning: A hybrid approach for elderly discharge planning.

Large-scale ontology storage and query using graph database-oriented approach: The case of Freebase (2016)
Presentation / Conference Contribution

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, t... Read More about Large-scale ontology storage and query using graph database-oriented approach: The case of Freebase.