bluuebunny/crossref_metadata_2025 huggingface dataset:
Improving Health IT through Understanding the Cultural Production of Safety in Clinical Settings
Authors: Novak Laurie Lovett
Published: February 2025

Health IT is said to have the potential to improve the safety and effectiveness of care. However, it is known that the implementation of health IT can introduce new risks into the environment of care as a result of design failures, implementation failures, and unintended consequences. The design and implementation of health IT systems reflect explicit or implicit assumptions about what constitutes safe practice. These assumptions may clash with those held by patients and clinicians who are the intended users and subjects of the technology. Current perspectives on risk are discussed and an anthropological approach to understanding the construction of safe practices in the clinical setting is explored using research in barcode medication administration.
In Vitro Model System To Study Mouse Intervertebral Disc Growth
Authors: Dahia Chitra  , Mahoney Eric  , Wall Eric  , Wylie Christopher
Published: February 2025

Introduction: Degenerative disc disease is the leading cause of disability and back pain. However the molecular signals that guide the normal development, and those involved in the degeneration of disc are not well defined. Nucleus pulposus (NP) cells of the disc are known to originate from embryonic notochord that act during embryogenesis as an important signaling center in patterning of neural crest and somites.
Pelvic Alignment and Leg Discrepancy in Adolescents with Idiopathic Scoliosis
Authors: Pasha Saba  , Sangole Archana P.  , Aubin Carl-Eric  , Mac Thiong Jean-Marc  , Labelle Hubert  , Parent Stefan
Published: February 2025

Introduction: Leg discrepancy is known as a symptomatic skeletal asymmetric in scoliosis. We hypothesized that leg discrepancy is correlated to the pelvic frontal alignment which in turn is correlated to the entire spinal deformities.
3D visualization tool for minimally invasive discectomy assistance
Authors: Bisson Martin  , Cheriet Farida  , Parent Stefan
Published: February 2025

Multimodal fusion of 2D thoracoscopic images with a pre-operative 3D anatomical model of the spine is useful for minimally invasive surgical procedures using an angled monocular endoscope with varying focal length. An offline calibration procedure has been developed to compute initial endoscope parameters, such as lens distortion, focal length and optical center before surgery. An optical tracking system is used to update extrinsic parameters describing the position and orientation of the endoscope in real-time during the procedure. This calibration allows the registration of the thoracoscopic image sequence with a pre-operative MRI 3D model of the spine. Two visualization methods merging the 3D model and thoracoscopic image sequence have been developed using both augmented reality and augmented virtuality paradigms primarily as an aid for discectomy. Augmented views are generated by adding annotations and projecting the MRI 3D model onto real thoracoscopic images. Virtual views are generated by projecting the real thoracoscopic images on a virtual view of the 3D model. Experimental results showed that the calibration procedure accuracy obtained by computing the relative 3D reconstruction error on a known object was 1.0 mm. Two orthopedic surgeons assessed the generated views, confirming the relevance and added value of the proposed visualization tool for minimally invasive discectomy assistance.
Design for Enterprise 2.0: Articulating the Online Social Capital Hypothesis
Authors: Bogg Paul  , Finnegan Patrick  , Land Lesley  , Barney Hamish
Published: February 2025

While the potential advantages of Web 2.0 tools for improving organizational collaboration, innovation and knowledge management is recognized, there is little understanding of how developers should design Enterprise 2.0 applications and processes to leverage the collective intelligence of web-based communities. This paper focuses on one form of Enterprise 2.0 application, social networking software, and explores design challenges emerging from the nature of the software. We argue the importance of ‘sociality’ rather than ‘functionality’ as the key design concept, and highlight challenges in relation to the identification of users, the specification of user requirements, and the nature of success in relation to social networking software. Drawing on research on the successful development of online communities, we posit that the success of social networking software can be regarded as the degree to which it facilitates the development of social capital in an online environment. We then use the theory of network governance and an analysis of social networking software to hypothesize the factors that lead to the development of online social capital. Working from this model, the paper concludes that the design of social networking software should focus on restricting access, allowing participants to assess the reputation of others, and implementing collective sanctions for those that violate the norms and values developed by the community.
PRIOR-WEB: A discrete multicriteria prioritization tool for the global economy context
Authors: Aguarón Juan  , Escobar María Teresa  , Moreno-Jiménez José María  , Turón Alberto
Published: February 2025

This work presents the updating to the global economy context of the discrete multicriteria prioritization tool, based on the Analytic Hierarchy Process, which is known as PRIOR [1]. The new decisional tool (PRIOR-Web) allows, using the internet, the secure incorporation of the judgments of the spatially distributed actors implicated in the resolution process. Jointly with the previous treatment of uncertainty developed by means of interval judgment and preference structures, the inclusion of functionalities as the priority and consistency stability intervals, the consistency consensus matrix and group decision making options provide new information for the construction of agreement paths between the participants involved in any multiactor decision making problem. A numerical example is used for illustrating how this social software works.
Public Procurement as Regulated Decision Making Problem: Challenges for DSS
Authors: Csáki Csaba  , Adam Frédéric
Published: February 2025

In recent times, managers have been faced with increasing levels of regulations which constrain their decision making and introduce unprecedented requirements for transparency and compliance. Public Procurement (PP), which is an important economic activity, as well as a powerful governmental policy tool, is one area where the level of regulation has reached unparalleled proportions with serious implications for decision makers and organisations. However, current literature does not offer an adequate discussion of public procurement as a decision making problem. This paper looks at PP in the context of Hungary to try and indentify the implications of the PP European and Hungarian legal frameworks for decision makers and for developers of decision support tools. The paper concludes that the highly regulatory setting for PP creates for decision makers, a normative framework which goes well beyond simple constraints on how to select suppliers. This in turn poses challenges for DSS which are discussed.
A System to Analyse Interviews and Structure the Acquired Knowledge Elements
Authors: Norese Maria Franca  , Salassa Fabio
Published: February 2025

When facing decisional problems in a public domain, the organization players often have a specific knowledge of their area of work and most of the time this know-how is neither recognized nor mapped for future utilization. In many situations, the good practice of knowledge documentation or experience collection through pre-established rules are not taken into due account; knowledge transfer is not always a priority for organizations. When the need to record and track the point of view of the actors in organization processes becomes a key point for the decisional structure, a methodology to easily and transparently produce a map of the situation is not available. The presented work concerns the acquisition and use of organizational knowledge in a multi actor public context, the university sector, in relation to a radical change called the “Bologna Process”. The aim of the work was to understand students' perception of the new courses supplied by an engineering faculty and to structure a set of organized and useable information elements pertaining to the main problematic areas, from the students' point of view. Free interviews appeared to be the most effective and easiest way of gathering the needed information elements, but the organization of these interviews and the treatment of all the acquired information elements required the activation of a planned and transparent procedure. The aim of this paper is to propose a methodological answer to the problem of acquiring, analysing, structuring and presenting knowledge and information obtained from a set of free interviews by demonstrating a case study. With the proposed cognitive mapping approach, it was possible validate, synthesize and visualize what emerged during the interviews. Moreover, when suggestions on a specific subject were made, the methodology allowed them to be collected, organized and reported to the diverse decisional structure players who were involved in the specific decision. The proposed procedure is a subset of the activities that are included in MACRAME, a decision support system that can be applied when a multi-actor and ill-structured situation needs to be analysed in order to generate a clear problem formulation, a structured and reliable model of the problem and the application of the model to evaluate and propose decision actions or solutions.
Probabilistic Ontologies for Multi-INT Fusion
Authors: Laskey Kathryn Blackmond  , Costa Paulo C.G.  , Janssen Terry
Published: February 2025

Systems are increasingly required to fuse data from geographically dispersed, heterogeneous information sources to produce up-to-date, mission-relevant results. These products focus not only on traditional military forces and systems, but to an increasing degree also on non-traditional combatants and their social networks. Successful multi-INT fusion requires that the constituent systems interoperate not just at the level of syntax and formats, but also at the level of semantics. Ontologies are vital enablers for semantic interoperability. Because uncertainty is a fundamental aspect of multi-INT fusion, lack of support for uncertainty is a major limitation of current-generation ontology formalisms. Probabilistic OWL (PR-OWL) extends the OWL Web Ontology Language to enable the construction of probabilistic ontologies. Ontologies constructed in PR-OWL can represent complex patterns of evidential relationships among uncertain hypotheses. Recently, a system for specifying and reasoning with PR-OWL ontologies has been released in beta version. This paper describes the PR-OWL ontology language, the probabilistic logic on which it is based, and the reasoning system implementation. A hypothetical case study in the counterterrorism domain illustrates the capabilities of PR-OWL.
Characterization of antigenetic serotypes from the dengue virus in Venezuela by means of Grid Computing
Authors: Isea Raúl  , Montes Esther  , Rubio-Montero Antonio J.  , Rosales José D.  , Rodríguez-Pascual Manuel A.  , Mayo Rafael
Published: February 2025

This work determines the molecular epidemiology of dengue virus in Venezuela by means of phylogenetic calculations performed on the EELA-2 Grid infrastructure with the PhyloGrid application, an open source tool that allows users performing phylogeny reconstruction in their research. In this study, a total of 132 E nucleotide gene sequences of dengue virus from Venezuela recorded in GenBank® have been processed in order to reproduce and validate the topology described in the literature.
Visualization of disease distribution with SNOMED CT and ICD-10
Authors: Nyström Mikael  , Vikström Anna  , Nilsson Gunnar  , Örman Håkan  , Åhlfeldt Hans
Published: February 2025

Methods for presentation of disease and health problem distribution in a health care environment rely among other things on the inherent structure of the controlled terminology used for coding. In the present study, this aspect is explored with a focus on ICD-10 and SNOMED CT. The distribution of 2,5 million diagnostic codes from primary health care in the Stockholm region is presented and analyzed through the “lenses” of ICD-10 and SNOMED CT. The patient encounters, originally coded with a reduced set of ICD-10 codes used in primary health care in Sweden, were mapped to SNOMED CT concepts through a mapping table. The method used for utilizing the richer structure of SNOMED CT as compared to ICD-10 is presented, together with examples of produced disease distributions. Implications of the proposed method for enriching a traditional classification such as ICD-10 through mappings to SNOMED CT are discussed.
Towards an implicit treatment of periodically-repeated medical data
Authors: Stantic Bela  , Terenziani Paolo  , Sattar Abdul  , Bottrighi Alessio  , Governatori Guido
Published: February 2025

Temporal information plays a crucial role in medicine, so that in Medical Informatics there is an increasing awareness that suitable database approaches are needed to store and support it. Specifically, a great amount of clinical data (e.g., therapeutic data) are periodically repeated. Although an explicit treatment is possible in most cases, it causes severe storage and disk I/O problems. In this paper, we propose an innovative approach to cope with periodic medical data in an implicit way. We propose a new data model, representing periodic data in a compact (implicit) way, which is a consistent extension of TSQL2 consensus approach. Then, we identify some important types of temporal queries, and present query answering algorithms to answer them. We also sketch a temporal relational algebra for our approach. Finally, we show experimentally that our approach outperforms current explicit approaches.
Pediatric Pain Management Knowledge Linkages: Mapping Experiential Knowledge to Explicit Knowledge
Authors: Stewart Sam  , Abidi Syed Sibte Raza  , Finley Allen
Published: February 2025

The goal of this project is to augment clinician communication by connecting it to evidence-based research, providing explicit knowledge to corroborate the experiential knowledge shared between health care practitioners. The source of tacit knowledge sharing is the Pediatric Pain Mailing List (PPML), a forum for practicing clinicians to contact peers on the subject of pain in children. The messages, dating back to 1993, are processed for pertinent information and gathered together into threads. They are then parsed and connected to a set of MeSH keywords, which is used to search Pubmed and return a set of papers that correspond to the subject being discussed. The results are presented in an online forum, providing clinicians with an arena in which they can browse the archives of the PPML and connect those conversations to pertinent medical literature.
Steps towards Single Source - Collecting Data about Quality of Life within Clinical Information Systems
Authors: Fritz Fleur  , Ständer Sonja  , Breil Bernhard  , Dugas Martin
Published: February 2025

Information about the quality of life from patients being treated in routine medical care is important for the attending physician. This data is also needed in research for example to evaluate the therapy and the course of the disease respectively. Especially skin diseases often negatively affect the quality of life. Therefore we aimed to design a concept to collect such data during treatment and use it for both medical care and research in the setting of dermatology. We performed a workflow analysis and implemented a designated form using the tools of the local clinical information system. Quality of life data is now collected within the clinical information system during treatment and is used for discharge letters, progress overviews as well as research about the treatment and course of disease. This concept which contributes to the single source approach was feasible within dermatology and is ready to be expanded into other domains.
Pathways Home: Comparing Voluntary IT and Non-IT Users Participating in a Mentored Self-Management Project
Authors: Cummings Elizabeth  , Robinson Andrew  , Pratt Helen Courtney  , Cameron-Tucker Helen  , Wood-Baker Richard  , Walters E. Haydn  , Turner Paul
Published: February 2025

This research paper examines the challenges in the development and adoption of an electronic patient diary within the Pathways Home for Respiratory Illness Project. This project supported community-based patients suffering from chronic obstructive pulmonary disease (COPD) to achieve increased levels of self-management and self-efficacy using electronic-monitoring techniques and mentoring by community health nurses. Participants had the option of voluntarily adopting an electronic patient diary to support their self-monitoring, which provided patients, nurses and clinicians with access to symptom and psycho-social data. This aimed to improve the identification, comprehension and initiation of early action in relation to alterations in their conditions. The paper presents data on technology adoption, electronic diary usage and, self-reported data quality, as well as examining the impact of the technology on hospitalisations (frequency and duration). The participants who chose to use the online patient diary continued their involvement with the project for the entire trial period (85% vs 54% completion). Participants were more likely to maintain use of the online patient diary than the paper diary. Both the groups experienced a positive improvement in their self-efficacy to self-manage their condition scores. The data highlight the problems implicit in some of the assumptions underpinning existing information systems models, especially in evaluating impact and the end-points presumed to be relevant in systems development life cycles.
Usage and Effect of a Web-based Intervention for the Prevention of Overweight; a RCT
Authors: Kelders Saskia M.  , van Gemert-Pijnen Julia E.W.C.  , Werkman Andrea  , Seydel Erwin R.
Published: February 2025

Web-based interventions can be effective in changing behaviour of people faced with health problems. However, it is unclear whether they are effective in preventing health problems like overweight. The aim of this study was to investigate usage and effectiveness of the Healthy Weight Assistant (HWA), a web-based application to increase healthy behaviour in adults with a healthy weight or slight overweight, by means of a Randomised Controlled Trial (RCT). 297 respondents were randomly assigned to the intervention (n=147) or the waiting list group (n=150). The intervention group received access to the intervention for 12 weeks. At pre- and post-test we measured dietary and physical activity behaviour (primary outcomes) and BMI, knowledge, attitude, self-efficacy, subjective behaviour and insight in behaviour (secondary outcomes). All participants, regardless of group, show improvement in healthy behaviour and subjective assessment of healthy behaviour. People who are older, score higher on dietary behaviour and under-estimate their dietary behaviour are more likely to use the HWA. Using the HWA leads to improvement in physical activity behaviour and insight in physical activity behaviour.
Using Electronic Medical Records for HIV Care in Rural Rwanda
Authors: Amoroso Cheryl L.  , Akimana Benjamin  , Wise Benjamin  , Fraser Hamish S.F.
Published: February 2025

Partners In Health (PIH) implemented an electronic medical record (EMR) system in Rwanda in 2005 to support and improve HIV and TB patient care. The system holds detailed patient records, accessible to clinicians through printed reports or directly via a computer in the consultation rooms. Ongoing assessment of data quality and clinical data use has led multiple interventions to be put in place. One such evaluation cycle led to the implementation of a system which identified 15 previously undiagnosed pediatric patients with HIV. Another cycle led to an EMR intervention which helped to decrease the proportion of completed critical CD4 lab results that did not reach clinicians by 34.2% (p=.002). Additionally an automated data quality improvement system reduced known errors by 92% by providing local data officers a tool and training to allow them to easily access and correct data errors. Electronic systems can be used to support care in rural resource-poor settings, and frequent assessment of data quality and clinical use of data can be used to support that goal.
Electronic Surveillance of Healthcare-Associated Infections with MONI-ICU – A Clinical Breakthrough Compared to Conventional Surveillance Systems
Authors: Koller Walter  , Blacky Alexander  , Bauer Claudia  , Mandl Harald  , Adlassnig Klaus-Peter
Published: February 2025

Surveillance of clinical entities such as healthcare-associated infections (HCAI) by conventional techniques is a time-consuming task for highly trained experts. Such are neither available nor affordable in sufficient numbers on a permanent basis. Nevertheless, expert surveillance is a key parameter for good clinical practice, especially in intensive care medicine. MONI-ICU (monitoring of nosocomial infections in intensive care units) has been developed methodically and practically in a stepwise manner over the last 20 years and is now a reliable tool for clinical experts. It provides an almost real-time view of clinical indicators for HCAI – at the cost of almost no additional time on the part of surveillance staff or clinicians. We describe the use of this system in clinical routine and compare the results generated automatically by MONI-ICU with those generated in parallel by trained surveillance staff using patient chart reviews and other available information (“gold standard”). A total of 99 ICU patient admissions representing 1007 patient days were analyzed. MONI-ICU identified correctly the presence of an HCAI condition in 28/31 cases (sensitivity, 90.3%) and their absence in 68/68 of the non-HCAI cases (specificity, 100%), the latter meaning that MONI-ICU produced no “false alarms”. The time taken for conventional surveillance at the 52 ward visits was 82.5 hours. MONI-ICU analysis of the same patient cases, including careful review of the generated results required only 12.5 hours (15.2%).
CEMARA an information system for rare diseases
Authors: Landais Paul  , Messiaen Claude  , Rath Ana  , Le Mignot Loïc  , Dufour Eric  , Ben Said Mohamed  , Jais Jean-Philippe  , Toubiana Laurent  , Baujat Geneviève  , Bourdon-Lanoy Eva  , Gérard-Blanluet Marion  , Bodemer Christine  , Salomon Rémi  , Aymé Ségolène  , Le Merrer Martine  , Verloes Alain
Published: February 2025

Rare diseases cover a group of conditions characterized by a low prevalence, affecting less than 1 in 2,000 people; 5000 to 7000 rare diseases have been currently identified in Europe. Most diseases do not have any curative treatment. They represent thus an important public health concern. CEMARA is based on a n-tier architecture. Its main objective is to collect continuous and complete records of patients with rare diseases, and their follow-up through a web-based Information System, and to analyse the epidemiological patterns. In France, 41 out of 131 labelled Reference Centres (RC) are sharing CEMARA. Presently 56,593 cases have been registered by more than 850 health care professionals belonging to 171 clinical sites. The national demand of care was explored in relation with the offer of care in order to reach an improved match. Within 2 years, CEMARA stimulated sharing a common platform, a common ontology with Orphanet and initiating new cohorts of rare diseases for improving patient care and research.
Traffic Accident In Cuiabá-Mt: An Analysis Through The Data Mining Technology
Authors: Galvão Noemi Dreyer  , de Fátima Marin Heimar
Published: February 2025

The traffic road accidents (ATT) are non-intentional events with an important magnitude worldwide, mainly in the urban centers. This article aims to analyzes data related to the victims of ATT recorded by the Justice Secretariat and Public Security (SEJUSP) in hospital morbidity and mortality incidence at the city of Cuiabá-MT during 2006, using data mining technology. An observational, retrospective and exploratory study of the secondary data bases was carried out. The three database selected were related using the probabilistic method, through the free software RecLink. One hundred and thirty-nine (139) real pairs of victims of ATT were obtained. In this related database the data mining technology was applied with the software WEKA using the Apriori algorithm. The result generated 10 best rules, six of them were considered according to the parameters established that indicated a useful and comprehensible knowledge to characterize the victims of accidents in Cuiabá. Finally, the findings of the associative rules showed peculiarities of the road traffic accident victims in Cuiabá and highlight the need of prevention measures in the collision accidents for males.
Investigating the potential of e-Learning in healthcare postgraduate curricula: A structural equation model
Authors: Katharaki Maria  , Daskalakis Stelios  , Mantas John
Published: February 2025

The objective of this paper is to assess the future adaptability of e-Learning platforms within postgraduate modules. An ongoing empirical assessment was conducted amongst postgraduate students, based on the Technology Acceptance Model (TAM). The current paper presents the outcomes from the second phase of a survey, involving fifty six participants. Data analysis was performed using a structural equation model, based on partial least squares. Results highlighted the very strong effect of perceived usefulness and perceived ease of use to attitude towards using e-Learning platforms. Consequently, attitude towards use proved to be a very strong predictor of behavioral intention. Perceived usefulness, on the contrary, did not prove to have an effect to behavioral intention. Implications on the potential of using e-Learning platforms are discussed along with limitations and future directions of the study.
Integration of Cognitive and Physical Training in a Smart Home Environment for the Elderly People
Authors: Konstantinidis Evdokimos I.  , Billis Antonis  , Hlauschek Walter  , Panek Paul  , Bamidis Panagiotis D.
Published: February 2025

Our research work is towards a service that can support senior citizens towards their independent living and active ageing. As it is suggested, physical and cognitive exercise training can contribute to a significant prolongation of personal autonomy and participation in society across prevailing age-related impairments such as cognitive decline. In the current paper, the approach of combination of both physical and cognitive training – adopted by LLM project – is discussed related to other similar projects that have taken place in the area of elderly home care and training. The aim of this work is to describe the technical design details of the integration process of the LLM service, which is based on a Web service architecture and to discuss alternative interface elements to be included in the LLM platform in terms of enabling user accessibility and acceptance.
A Business case for HIT Adoption: effects of “meaningful use” EHR financial incentives on clinic revenue
Authors: Behkami Nima A.  , Dorr David A.  , Morrice Stuart
Published: February 2025

The goal of this study is to describe a framework that allows decision makers to efficiently evaluate factors that affect Electronic Health Record (EHR) adoption and test suitable interventions; specifically financial incentives. The United States healthcare delivery system is experiencing a transformation to improve population health. There is strong agreement that “meaningful use” of Health Information Technology (HIT) is a major enabler in this effort. However it's also understood that the high cost of implementing an EHR is an obstacle for adoption. To help understand these complexities we developed a simulation model designed to capture the dynamic nature of policy interventions that affect the adoption of EHR. We found that “Effective” use of HIT approaches break-even-point and larger clinic revenue many times faster that “average” or “poor” use of HIT. This study uses a systems perspective to the evaluate EHR adoption process through the “meaningful use” redesign as proposed in the American Reinvestment and Recovery Act 2009 in the United States healthcare industry by utilizing the System Dynamics methodology and Scenario Analysis.
MET3-AE System to Support Management of Pediatric Asthma Exacerbation in the Emergency Department
Authors: Wilk Szymon  , Michalowski Wojtek  , Farion Ken  , Sayyad Shirabad Jelber
Published: February 2025

A decision making process behind the management of pediatric patients with asthma exacerbations in the Emergency Department includes three stages: data collection, diagnosis formulation and treatment planning. These stages are associated with activities involving different types of clinical knowledge: factual, conceptual and procedural. Effective decision support should span over the entire decision making process and facilitate the use of diversified clinical knowledge. In this paper we present MET3-AE – a point of care decision support system that satisfies this requirement. The system helps emergency physician collect data, evaluate exacerbation severity, plan corresponding treatment and retrieve clinical evidence associated with a given treatment plan. It was developed using ontology-driven and multi-agent methodologies and implemented with open source software. The system is accessible on tablet and desktop computers and smartphones, and it interacts with other hospital information systems. It was successfully verified in a simulated clinical setting and now it is undergoing testing in a teaching hospital.
Citizen Centric Architecture Approach - Taking e-health forward by integrating citizens and service providers
Authors: Han Yong  , Itälä Timo  , Hämäläinen Matti
Published: February 2025

In this paper, two related research problems will be discussed in the development of e-health services: First, an architectural approach is needed to provide a holistic view for solving the ICT challenges in e-health development. Second, solving the needs of the citizens should be the focus of the architecture solution. To overcome these problems we suggest a Citizen Centric Architecture (CCA) approach for providing a holistic and appropriately balanced view of the integration. Naturally, enterprises' information systems and citizens' information systems are the key elements of CCA. In addition, for solving the topology challenge brought by a large number of involved parties, a role of trusted third party is proposed to provide an environment for the information exchange and service mediation among the various parties. We believe this approach will enable the large scale growth in citizen centric e-health services that is poorly facilitated by the prevailing models: the improved integration of information will attract more citizens and health care service providers, which in turn, improve the health care information and quality of service.
Semantic Reasoning with XML-based Biomedical Information Models
Authors: O'Connor Martin J.  , Das Amar
Published: February 2025

The Extensible Markup Language (XML) is increasingly being used for biomedical data exchange. The parallel growth in the use of ontologies in biomedicine presents opportunities for combining the two technologies to leverage the semantic reasoning services provided by ontology-based tools. There are currently no standardized approaches for taking XML-encoded biomedical information models and representing and reasoning with them using ontologies. To address this shortcoming, we have developed a workflow and a suite of tools for transforming XML-based information models into domain ontologies encoded using OWL. In this study, we applied semantics reasoning methods to these ontologies to automatically generate domain-level inferences. We successfully used these methods to develop semantic reasoning methods for information models in the HIV and radiological image domains.
ANTIPA: an agent architecture for intelligent information assistance
Authors: Oh Jean  , Meneguzzi Felipe  , Sycara Katia
Published: February 2025

Human users trying to plan and accomplish information-dependent goals in highly dynamic environments with prevalent uncertainty must consult various types of information sources in their decision-making processes while the information requirements change as they plan and re-plan. When the users must make time-critical decisions in information-intensive tasks they become cognitively overloaded not only by the planning activities but also by the information-gathering activities at various points in the planning process. We have developed the ANTicipatory Information and Planning Agent (ANTIPA) to manage information adaptively in order to mitigate user cognitive overload. To this end, the agent brings information to the user as a result of user requests but most crucially, it proactively predicts the user's prospective information needs by recognizing the user's plan; pre-fetches information that is likely to be used in the future; and offers the information when it is relevant to the current or future planning decisions. This paper introduces a fully implemented agent of the ANTIPA architecture using a decision-theoretic user model.
Knowledge-based adaptive thresholding from shadows
Authors: Santos Paulo  , Dee Hannah M.  , Fenelon Valquiria
Published: February 2025

This paper presents results of a mobile robot qualitative self-localisation experiment using information from cast shadows. We present results of self-localisation using two methods for obtaining the threshold automatically: in one method the images are segmented according to their grey-scale histograms, in the other the threshold is set according to a prediction about the robot's location, given a shadow-based map defined upon a qualitative spatial reasoning theory. To the best of our knowledge this is the first work that uses qualitative spatial representations both to perform egolocation and to calibrate a robot's interpretation of its perceptual input.
Mining Physiological Data for Discovering Temporal Patterns on Disease Stages
Authors: Loglisci Corrado  , Ceci Michelangelo  , Malerba Donato
Published: February 2025

Analyzing physiological data can be of great importance in unearthing information on the course of a disease. In this paper we propose a data mining approach to analyze these data and acquire knowledge, in the form of temporal patterns, on the physiological events which can frequently trigger particular stages of disease. The application to the sleep sickness scenario is addressed to discover patterns, expressed in terms of breathing and cardiovascular system time-annotated disorders, which may trigger particular sleep stages.
High-Level Perception as Focused Belief Revision
Authors: Ismail Haythem  , Kasrin Nasr
Published: February 2025

We present a framework for incorporating perception-induced beliefs into the knowledge base of a rational agent. Normally, the agent accepts the propositional content of perception and other propositions that follow from it. Given the fallibility of perception, this may result in contradictory beliefs. Hence, we model high-level perception as belief revision. We overcome difficulties imposed by the highly idealistic classical belief revision in two ways. First, we adopt a belief revision operator based on relevance logic, thus limiting the derived beliefs to those that relevantly follow from the new percept. Second, we focus belief revision on only a subset of the agent's set of beliefs—those that we take to be within the agent's current focus of attention.
Bayesian Monte Carlo for the Global Optimization of Expensive Functions
Authors: Groot Perry  , Birlutiu Adriana  , Heskes Tom
Published: February 2025

In the last decades enormous advances have been made possible for modelling complex (physical) systems by mathematical equations and computer algorithms. To deal with very long running times of such models a promising approach has been to replace them by stochastic approximations based on a few model evaluations. In this paper we focus on the often occuring case that the system modelled has two types of inputs x = (xc, xe) with xcrepresenting control variables and xerepresenting environmental variables. Typically, xcneeds to be optimised, whereas xeare uncontrollable but are assumed to adhere to some distribution. In this paper we use a Bayesian approach to address this problem: we specify a prior distribution on the underlying function using a Gaussian process and use Bayesian Monte Carlo to obtain the objective function by integrating out environmental variables. Furthermore, we empirically evaluate several active learning criteria that were developed for the deterministic case (i.e., no environmental variables) and show that the ALC criterion appears significantly better than expected improvement and random selection.
Data Mining for Biodiversity Prediction in Forests
Authors: O'Sullivan Barry  , Keady Steven  , Keane Enda  , Irwin Sandra  , O'Halloran John
Published: February 2025

There is international consensus on the key elements of sustainable forest management. Biological diversity has been recognised as one of them. This paper investigates the usefulness of terrestrial laser scanning technology in forest biodiversity assessment. Laser scanning is a rapidly emerging technology that captures high-resolution, 3-D structural information about forests and presently has applications in standing timber measurement. Forest biodiversity is influenced by structural complexity in the forest although precise repeatable measures are difficult to achieve using traditional methods. The aim of the research presented here is to apply laser scanning technology to the assessment of forest structure and deadwood, and relate this information to the diversity of plants, invertebrates and birds in a range of forest types including native woodlands and commercial plantations. Procedures for forest biodiversity assessment are known to be expensive due to their reliance on labour-intensive field visits. We describe our progress on the application of terrestrial laser scanning in an automated approach to biodiversity assessment. We apply regression techniques from the field of data mining to predict several biodiversity measures using physical attributes of the forest with very promising results.
Tractable Reasoning with DL-Programs over Datalog-rewritable Description Logics
Authors: Heymans Stijn  , Eiter Thomas  , Xiao Guohui
Published: February 2025

The deployment of KR formalisms to the Web has created the need for formalisms that combine heterogeneous knowledge bases. Nonmonotonic dl-programs provide a loose integration of Description Logic (DL) ontologies and Logic Programming (LP) rules with negation, where a rule engine can query an ontology with a native DL reasoner. However, even for tractable dl-programs, the overhead of an external DL reasoner might be considerable. To remedy this, we consider Datalog-rewritable DL ontologies, i.e., ones that can be rewritten to Datalog programs, such that dl-programs can be reduced to Datalog¬, i.e, Datalog with negation, under well-founded semantics. To illustrate this framework, we consider several Datalog-rewritable DLs. Besides fragments of the tractable OWL 2 Profiles, we also present ℒ𝒟ℒ+as an interesting DL that is tractable while it has some expressive constructs. Our results enable the usage of DBLP technology to reason efficiently with dl-programs in presence of negation and recursion, as a basis for advanced applications.
Uncertainty Propagation for Efficient Exploration in Reinforcement Learning
Authors: Hans Alexander  , Udluft Steffen
Published: February 2025

Reinforcement learning aims to derive an optimal policy for an often initially unknown environment. In the case of an unknown environment, exploration is used to acquire knowledge about it. In that context the well-known exploration-exploitation dilemma arises—when should one stop to explore and instead exploit the knowledge already gathered? In this paper we propose an uncertainty-based exploration method. We use uncertainty propagation to obtain the Q-function's uncertainty and then use the uncertainty in combination with the Q-values to guide the exploration to promising states that so far have been insufficiently explored. The uncertainty's weight during action selection can be influenced by a parameter. We evaluate one variant of the algorithm using full covariance matrices and two variants using an approximation and demonstrate their functionality on two benchmark problems.
The Dynamics of Multi-Agent Reinforcement Learning
Authors: Dickens Luke  , Broda Krysia  , Russo Alessandra
Published: February 2025

Infinite-horizon multi-agent control processes with non-determinism and partial state knowledge have particularly interesting properties with respect to adaptive control, such as the non-existence of Nash Equilibria (NE) or non-strict NE which are nonetheless points of convergence. The identification of reinforcement learning (RL) algorithms that are robust, accurate and efficient when applied to these general multi-agent domains is an open, challenging problem. This paper uses learning pressure fields as a means for evaluating RL algorithms in the context of multi-agent processes. Specifically, we show how to model partially observable infinite-horizon stochastic processes (single-agent) and games (multi-agent) within the Finite Analytic Stochastic Process framework. Taking long term average expected returns as utility measures, we show the existence of learning pressure fields: vector fields – similar to the dynamics of evolutionary game theory, which indicate medium and long term learning behaviours of agents independently seeking to maximise this utility. We show empirically that these learning pressure fields are followed closely by policy-gradient RL algorithms.
An Efficient Procedure for Collective Decision-making with CP-nets
Authors: Li Minyi  , Vo Quoc Bao  , Kowalczyk Ryszard
Published: February 2025

This paper studies the problem of collective decision-making in the case where the agents' preferences are represented by CP-nets (conditional preference networks). In many real-world decision-making problems, the number of possible outcomes is exponential in the number of domain variables. Most related works either do not consider computational concerns, or depend on a strong assumption that all the agents' CP-nets share a common preferential-independence structure. To this end, we introduce a novel procedure for collective decision-making with CP-nets. Our proposed approach allows the agents to have different preferential-independence structures and guarantees Pareto-optimality. Our experimental results demonstrate that our proposed procedure is computationally efficient and produces the results that are close to the fair Minimax solution.
An Iterative A* Algorithm for Planning of Airport Ground Movements
Authors: Lesire Charles
Published: February 2025

Optimization of ground traffic is a major issue of air traffic management: optimal ground circulation could decrease flight delays and consequently decrease costs and increase passenger wellness. This paper proposes a planning algorithm for ground traffic based on contract reservation. This algorithm is iterative: it plans aircraft itinerary one after the other. A first version is described using the classical A* algorithm. Then the model is extended to deal with time and speed uncertainty to ensure the feasibility of the planned trajectories while avoiding conflicts between aircrafts. Its efficiency is evaluated on Toulouse-Blagnac airport, regarding quality of the solution and computation times.
A Fault-model-based Debugging Aid for Data Warehouse Applications
Authors: Struss Peter  , Shivashankar Vikas  , Zahoor Mohamed
Published: February 2025

The paper describes a model-based approach to developing a general tool for localizing faults in applications of data warehouse technology. A model of the application is configured from a library of generic models of standard (types of) modules and exploited by a consistency-based diagnosis algorithm, originally used for diagnosing physical devices. Observing intermediate results can require high efforts or even be impossible, which limits the discriminability between different faults in a sequence of data processing steps. To compensate for this, fault models are used. This becomes a feasible solution for standard modules of a data warehouse application along with a stratification of the data. Fault models capture the potential impact of faults of process steps and data transfer on the data strata as well as on sets of data. Reflecting the nature of the initial symptoms and of the potential checks, these descriptions are stated at a qualitative level. The solution has been validated in customer report generation of a provider of mobile phone services.
Regression Learning with Multiple Noisy Oracles
Authors: Ristovski Kosta  , Das Debasish  , Ouzienko Vladimir  , Guo Yuhong  , Obradovic Zoran
Published: February 2025

In regression learning, it is often difficult to obtain the true values of the label variables, while multiple sources of noisy estimates of lower quality are readily available. To address this problem, we propose a new Bayesian approach that learns a regression model from data with noisy labels provided by multiple oracles. The proposed method provides closed form solution for model parameters and is applicable to both linear and nonlinear regression problems. In our experiments on synthetic and benchmark datasets this new regression model was consistently more accurate than a model trained with averaged estimates from multiple oracles as labels.
From bursty patterns to bursty facts: The effectiveness of temporal text mining for news
Authors: Subašić Ilija  , Berendt Bettina
Published: February 2025

Many document collections are by nature dynamic, evolving as the topics or events they describe change. The goal of temporal text mining is to discover bursty patterns and to identify and highlight these changes to better enable readers to track stories. Here, we focus on the news domain, where the changes revolve around novel, previously unpublished, “facts” that have an effect on the story developments. However, despite intense research activities on bursty patterns, a lack of common procedures today makes it impossible to compare methods in a principled way. To close this gap, we (a) investigate how different temporal text mining methods discover novel facts and (b) present an evaluation framework for methods assessment, consisting of a set of procedures and metrics for cross-evaluating models. Bursty patterns are transformed into queries for sentence retrieval, either with or without taking into account internal pattern structure, and these sentences are compared with a set of editor-selected ground-truth reference sentences. Our experiments on different classes of temporal text mining show that different methods perform at similar levels overall, but provide distinctive advantages in some settings. The experiments also demonstrate the benefits of using patterns' internal structure for query generation.