1,721,041 research outputs found
Emerging challenges in legal informatics from machine learning to LLMs - Preface to the proceedings of the 1st PLC workshop
No full textThe integration of Artificial Intelligence techniques, machine learning and large language models into legal informatics offers innovative potential, from enhancing legal research efficiency to supporting legal reasoning. These advancements introduce significant challenges, including issues related to data privacy, bias in legal datasets, and the interpretability of complex algorithms in legal contexts. Emerging challenges involve reliability, fairness, and ethical considerations in AI-driven legal applications. The research contributions presented at a recent workshop on Processes, Law and Compliance aim to deepen these issues for the development of AI applications in the field of legal informatics.</p
Improve Hospital Management Through Process Mining, Optimization, and Simulation: the CH4I-PM Project
Full text linkThe growing digitalization of society opens up the exploitation of new IT techniques in the healthcare sector. This report presents an application of AI techniques such as prediction, optimization, and automated knowledge extraction with process mining from hospital information system data. In addition, a simulation effort with Building Information Modeling and Agent-Based Modeling techniques has been performed. The present report describes practical cases and the lesson learned from planning, management, and coordination activities of the project as a whole
Exploring sentiment in social media and official statistics: A general framework
No full textThe integration between official statistics and social media data is a challenging topic. This contribution aims to present a recentlydesigned framework to compare sentiment analysis on social media content with social and economic data. Such framework-which has already been applied, in a preliminary fashion, to the Felicitta project-is meant to integrate official statistics and correlate it with online social media data. Its ultimate goal, in fact, namely consists in giving a contribution to the definition of a measure of subjective well-being that could fully benefit from both traditional, well-established social indicators and dynamic data obtained from the web
A survey on agents applications in healthcare: Opportunities, challenges and trends
No full textBackground and Objective: The agent abstraction is a powerful one, developed decades ago to represent crucial aspects of artificial intelligence research. The meaning has transformed over the years and now there are different nuances across research communities. At its core, an agent is an autonomous computational entity capable of sensing, acting, and capturing interactions with other agents and its environment. This review examines how agent-based techniques have been implemented and evaluated in a specific and very important domain, i.e. healthcare research. Methods: We survey key areas of agent-based research in healthcare, e.g. individual and collective behaviours, communicable and non-communicable diseases, and social epidemiology. We propose a systematic search and critical review of relevant recent works, introduced by an exploratory network analysis. Results: Network analysis enables to devise out 5 main research clusters, the most active authors, and 4 main research topics. Conclusions: Our findings support discussion of some future directions for increasing the value of agent-based approaches in healthcare
Standardization and harmonization of law through automated process analysis and similarity techniques
No full textIn comparative law, standardization and harmonization of law are interrelated concepts that aim to increase legal certainty, efficiency, and accessibility. Standardization focuses on creating uniform rules and procedures within a specific jurisdiction, while harmonization seeks to align legal frameworks across jurisdictions. This research aims to investigate the impact of standardization and harmonization on legal systems through the application of AI methods. Two case studies were analyzed using machine learning, natural language processing, and process mining techniques. The paper also reports a brief discussion of the methods adopted and early results from two legal domain experts.</p
Detecting Happiness in Italian Tweets: Towards an Evaluation Dataset for Sentiment Analysis in Felicittà
Full text linkThis paper focuses on the development of a gold standard corpus for the validation of Felicitta, an online platform which uses Twitter as data source in order to estimate and interactively display the degree of happiness in the Italian cities. The ultimate goal is the creation of an Italian reference Twitter dataset for sentiment analysis that can be used in several frameworks aimed at detecting sentiment from big data sources. We will provide an overview of the reference corpus created for evaluating Felicitta, with a special focus on the issues `
raised from its development, on the inter-annotator agreement discussion and on implications for the further development of the corpus, considering that the assumption that a single right answer exists for each annotated instance cannot be done in several cases in the particular kind of data at issue
Robust solutions via optimisation and predictive process monitoring for the scheduling of the interventional radiology procedures
Full text linkInterventional radiology (IR) is an increasingly used medical specialty relying on the possibilities offered by medical imaging guidance technologies to perform minimally invasive procedures (both diagnostic and therapeutic) through very small incisions or body orifices. Although the operative context is quite similar to that of the classical operating room (OR) literature, to the best of our knowledge management problems arising in the IR operative context never appeared in the healthcare management literature. This is even more true for studies that combine the OR approach with automatic extraction of information from real hospital health record data as in the present study. Two specific features characterise our case study with respect to the traditional OR literature: due to the Italian legislation, the anaesthetist (usually in a very limited number) must be present for the entire duration of the procedure (
), and the IR does not have its own ward but receives inpatients from different wards (
). The aim of this paper is to introduce a novel approach to determine a robust solution for our case study problem addressing both features
and
. Our approach is based on the interplay between optimisation and predictive process monitoring (PPM) models. The obtained results show that the proposed approach produces schedules that achieve higher usage rate, lower overtime and more patients operated on than the original schedule. We also show that the integration of PPM models within the optimisation workflow improves the quality of the output schedule with respect to the standard one-shot optimisation
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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