bluuebunny/crossref_metadata_2025 huggingface dataset:
Usability Testing of a mHealth App to Support Self-Management of HIV-Associated Non-AIDS Related Symptoms
Authors: Stonbraker Samantha  , Cho Hwayoung  , Hermosi Gabriella  , Pichon Adrienne  , Schnall Rebecca
Published: February 2025

Mobile health (mHealth) applications (apps) are uniquely poised to offer the information that persons living with HIV (PLWH) need to manage the symptoms associated with their chronic condition. The purpose of this study was to assess the usability of a mHealth app designed to help PLWH self-man-age the symptoms associated with their HIV and HIV-associated non-AIDS (HANA) conditions. We conducted a heuristic evaluation with five experts in informatics and end-user testing with 20 PLWH. End-users completed the PSSUQ and Health-ITUES validated measures of system usability. Mean severity scores for the 10-item heuristic checklist com-pleted by experts ranged from 0.4–2.4. End-users gave the system high scores on the PSSUQ and Health-ITUES usability measures (mean 2.23 ± 0.83 and 4.24 ± 0.62 respectively). Results indicated the system is usable and will be ready for future efficacy testing after incorporation of recommended feedback.
Patient Accessible Electronic Health Records: Impacts on Nursing Documentation Practices at a University Hospital
Authors: Fjola Smaradottir Berglind
Published: February 2025

In a Norwegian health region, patients have online access to their own electronic health record and they can also read the nursing documentation. This paper presents a qualitative study made at a university hospital to investigate how patient accessible electronic health records impact on nursing documentation practices. Semi-structured interviews were made with 12 informants from 5 cardiology departments at one hospital regarding how they used electronic nursing documentation in their daily practice and how they experienced patient accessible nursing documentation. The nurses emphasized that they focused on a clear and well-written nursing documentation, but in some situations, they were hesitant to write sensitive information. The study concluded that the implementation of patients' reading access to the electronic health record had limited impact on the nursing documentation and the daily practice at the departments, but the nursing handover had an even more important function for oral exchange of information.
Smartphone Apps for Pregnant Women: The Gap Between the Quality of Current Apps and Patients' Needs
Authors: Mao Lina  , Wang Jiani  , Liu Huanxia  , Xiao Qian
Published: February 2025

To explore the gap between women's needs for pregnancy related apps and to evaluate the quality of the top 10 downloaded pregnancy related apps, we designed a cross-sectional study to survey 153 pregnant women. This study showed pregnant women had high requirement against the quality of knowledge content, functions, interface and basic characteristics of apps. The top 10 popular apps showed a range of drawbacks and were far from satisfying the needs of pregnant women.
Development of Practical Teaching Model Based on Intelligent Comprehensive Nursing Practice Platform
Authors: Bai Yamei  , Huang Fang  , Wang Qiuqin  , Ying Haiyan  , Xu Guihua
Published: February 2025

To solve the problems existing in the traditional nursing practice teaching and enhance the training quality of nursing baccalaureate students. This article developed a nursing practice teaching model based on the intelligent information platform by integrating the methods of autonomous learning, inquiry learning, blended learning, and action teaching with constructivism learning theory in an attempt to realize the combination of information technology with experimental teaching in nursing baccalaureate program.
Interprofessional Informatics Curricula: A Literature Review
Authors: Byard Jillisa  , Currie Leanne M.
Published: February 2025

The aim of this literature review was to examine evidence related to interprofessional informatics education to support the design of an interprofessional informatics curriculum. Main themes from a total of twelve articles were synthesized and included use of technologies such as academic electronic health records; faculty enablers; curriculum implementation barriers; and student outcomes of current IP informatics education.
The Technology Use and Information Flow at a Municipal Telemedicine Service
Authors: Smaradottir Berglind  , Fensli Rune
Published: February 2025

Health care services facechallenges with providing individualised treatment to an ageing population prone to chronic conditions and multi-morbidities. The research project Patients and Professionals in Productive Teams aims to study patient-centred teamworkservice models. This paper presents an evaluation of a telemedicine service for chronic obstructive pulmonary disease patients integrated with municipal health care services. Qualitative methods were used to study the technology use and information flow. The results showed that the telemedicine technology was a standalone system, not integrated with the electronic health record of the municipality. A benefit of the system was a function to provide the patient with written instructions on agreements and advices. As a constraint for the patient-centred team approach, the information in the telemedicine system was available only for the telemedicine nurses and not to other health care professionals.
Design and Development of a Mobile Application Supporting Planning for Future Parents
Authors: Heldal Ilona  , Efrem Igor  , Helgesen Carsten
Published: February 2025

Life is changing after recognizing a pregnancy. Then the future parents, who are often healthy individuals, need to change their lifestyles, meet different healthcare institutions, follow new rules, and reschedule their everyday activities. This paper illustrates the design and development of a novel mobile application supporting future parents in planning their activities after recognizing the pregnancy. The focus is on identifying and sharing relevant data between future-parents, healthcare institutions, and the supporting social groups. First, the design and development of the application are sketched, and then we present early evaluation results with major stakeholders: future parents and responsible actors from healthcare institutions, based on data from interviews. The results contribute to increased understanding of developing mobile applications for future parents, considering temporal, situation dependent needs during the pregnancy process, but also after having given birth. To handle temporal and longer-term needs, and provide contextualized information, considering patient-pathways are beneficial.
Using Voice-Activated Conversational Interfaces for Reporting Patient Safety Incidents: A Technical Feasibility and Pilot Usability Study
Authors: Sun Owen  , Chen Jessica  , Magrabi Farah
Published: February 2025

Conversational interfaces and speech recognition capabilities are being increasingly used to create more natural and intuitive user interaction with digital technology. While voice-activated technologies have been used to support clinical documentation, their use for reporting patient safety incidents has not been previously investigated. The purpose of this paper is to assess the technical feasibility of an application for reporting incidents that combines a conversational interface with speech recognition software, and to undertake a pilot study of its usability. We built a prototype finite state-based application where incidents involving digital health technologies could be reported by answering five questions about the task being performed, the response of the software and the outcome of the clinical situation. Pilot tests showed that the conversational interface was usable. However, participants expressed concerns with speaking out loud about sensitive patient safety and quality improvement issues, including human error and system failures, in busy clinical environments. Further work is required to identify the clinical contexts in which conversational interfaces can be used to support incident reporting.
Privacy and the Internet of Things (IoT) Monitoring Solutions for Older Adults: A Review
Authors: Alkhatib Sami  , Waycott Jenny  , Buchanan George  , Bosua Rachelle
Published: February 2025

The rapid increase in the number of older adults in developed countries has raised concerns about their well-being and increasing need for healthcare. New technologies, including Internet of Things, are being used to monitor older adults' health and activities, thus enabling them to live safely and independently at home as they age. However, Internet of Things monitoring solutions create privacy challenges that need to be addressed. This review examines how privacy has been conceptualised in studies proposing new Internet of Things solutions for monitoring older adults. The literature reviewed mostly links privacy with information security and unauthorised accessibility threats. There is a limited consideration of other aspects of privacy such as confidentiality and secondary use of users' information. We argue that developers of Internet of Things solutions that aim to monitor and collect health data about older adults need to adopt an expanded view of privacy. This will ensure that safeguards are built in to Internet of Things devices to protect and maintain users' privacy while also enabling the appropriate sharing of data to support older adults' safety and wellbeing.
Requirements for Collaborative Decision Support Systems in Wound Care: No Information Continuity Without Management Continuity
Authors: Przysucha Mareike  , Vogel Stefan  , Hüsers Jens  , Wache Stefanie  , Sellemann Björn  , Hübner Ursula
Published: February 2025

Health IT systems are employed to support continuity of care via information continuity, while management continuity is often neglected. This study aims at investigating issues of management continuity when developing a collaborative decision support system for chronic wounds. Thirty-three experts from a variety of professions and disciplines discussed problems and possible solutions in four workshops. The following topics emerged from the discussion: existing networks involving payers, responsibilities as well as good discharge management. These topics clearly address management continuity and are also relevant for the scenario of inter-professional wound care across different settings.
Using RNA-Seq Data for the Detection of a Panel of Clinically Relevant Mutations
Authors: Wolff Alexander  , Perera-Bel Júlia  , Schildhaus Hans-Ulrich  , Homayounfar Kia  , Schatlo Bawarjan  , Bleckmann Annalen  , Beißbarth Tim
Published: February 2025

Somatic single nucleotide variants (SNVs) are genomic events with increasing implications in cancer treatment. The clinical standard for SNVs detection is whole genome/exome sequencing (WGS/WES) in matched tumor-normal samples. Yet, this is a very costly approach both economically and biologically and very often only tumor samples are sequenced. On the other hand, RNA sequencing (RNA-Seq) is the most popular technology to study gene expression, and has also the potential for a cost-effective identification of SNVs as an alternative to tumor-only WES. Here we present a method for the identification of SNVs in tumor-only RNA-Seq data putting a special focus on a small panel of clinically relevant SNVs. For evaluation purposeswe analyzed matched tumor-normal WEStumor-only RNA-Seq data from 14 cancer patients. We compared SNVs detected in i) RNA-Seq by our method, ii) WES tumor-only by Mutect2 and iii) WES matched tumor-normal by Mutect2. We did a detailed evaluation for a reduced panel of clinically relevant SNVs and reliably identified in RNA-Seq data a subset of mutations for which we had pathological annotation. Hence, RNA-Seq rises as a cost-effective option to detect in parallel gene expression as well as a small panel of clinically relevant SNVs in research.
Data Discovery for Integration of Heterogeneous Medical Datasets in the German Center for Lung Research (DZL)
Authors: Majeed Raphael W.  , Stöhr Mark R.  , Ruppert Clemens  , Günther Andreas
Published: February 2025

The German Centre for Lung Research (DZL) is an association of Germany's leading university and non-university institutions dedicated to lung research. Institutes and disease areas within the DZL manage their own data in several databases and registers using different software tools. Aim of our data integration effort is to provide a single central data warehouse frontend, where all patient related data is combined and made accessible. A two-stage survey was used to determine the data collections suitable for data integration. Integration was performed via extract-transform-load (ETL) steps using custom software. Original software (e.g. eCRF) used by the data collections did not need any modifications. The survey yielded 68 data collections. Until Jan 2018, 20 collections were successfully integrated. 10 collections were withdrawn by their owners while the integration of 38 was delayed. Data discovery, the process of finding existing data collections in a large research network, proved to be the step most underestimated. From technical point of view, data integration proved to be of minor complexity in comparison to the effort required for harmonization/mapping of data elements and management of common terminology.
The Nature of Twitter Trending Topics
Authors: Palomino Marco A.  , Ribac Quentin  , Masala Giovanni
Published: February 2025

We are inundated with data—companies such as Twitter deal with petabytes of information on a daily basis. However, some users, especially the new ones, often find it difficult to cope with the overwhelming and disorganised deluge of information. Scientists have already worked out ways to identify Twitter trending topics, as a means to index information and make sense of it. However, we know little about the impact on trending topics of various intrinsic factors associated with the Twitter ecosystem. For example, anecdotal evidence suggests that trending topics are characterised by highly polarised tweets, or that large audiences typically host the emergence of trending topics. However, no study has yet addressed these issues formally. To remedy this situation, we have launched an investigation on the nature of trending topics. Our initial observations indicate that there is a correlation between strong sentiment polarity and the emergence of trending topics—we can also confirm that the strength of the polarity drops as the trending topics fade away. Conversely, our experiments highlight that there is no correlation between the size of a Twitter audience and the rise of trending topics.
Model Driven Evolution of an Agent-Based Home Energy Management System
Authors: Ayala Inmaculada  , Amor Mercedes  , Horcas Jose-Miguel  , Fuentes Lidia
Published: February 2025

Advanced smart home appliances and new models of energy tariffs imposed by energy providers pose new challenges in the automation of home energy management. Users need some assistant tool that helps them to make complex decisions with different goals, depending on the current situation. Multi-agent systems have proved to be a suitable technology to develop self-management systems, able to take the most adequate decision under different context-dependent situations, like the home energy management. The heterogeneity of home appliances and also the changes in the energy policies of providers introduce the necessity of explicitly modeling this variability. But, multi-agent systems lack of mechanisms to effectively deal with the different degrees of variability required by these kinds of systems. Software Product Line technologies, including variability models, has been successfully applied to different domains to explicitly model any kind of variability. We have defined a software product line development process that performs a model driven generation of agents embedded in heterogeneous smart objects with different degrees of self-management. However, once deployed, the home energy assistant system has to be able to evolve to self-adapt its decision making or devices to new requirements. So, in this paper we propose a model driven mechanism to automatically manage the evolution of multi-agent systems distributed among several devices.
Using the AHP Model to Improve the Measurement of Satisfaction in the ICT Sector
Authors: Carrasco Ramón A.  , Forero Laura N.  , López Santiago X.  , Herrera-Viedma Enrique  , Porcel Carlos
Published: February 2025

The purpose of this paper is to adopt Analytical Hierarchy Process (AHP), as a technique to estimate the importance weights of the two-dimensional SICTQUAL model [1], to help recognize which of the sixteen service quality elements that compose the model, require more attention to achieve a competitive advantage in the ICT sector. From this, the decision problem is broken down into hierarchy in terms of overall objective, dimensions, and sub-dimensions, which allows pairwise comparisons to be made among the sub-dimensions with regard to the service dimensions [2]. This enables the decision makers of the ICT sector, design and maintain an improvement plan based on their preferences, to control customer satisfaction, due to its effect on business strategies. In particular, an application has been shown to the Spain's telecoms sector.
Data Dimension and Structure Effects in Predictive Performance of Deep Neural Networks
Authors: Urda Daniel  , Jerez Jose M.  , Turias Ignacio J.
Published: February 2025

Deep learning provides a variety of neural network based models, known as Deep Neural Networks (DNNs), which are being successfully used in several domains to build highly accurate predictors from data. In particular, the predictive performance of a dense fully-connected multi-layer neural networks may vary depending on some factors. In this paper, 18 synthetic datasets were used to test the effect of data dimension and data structure on the predictive performance of a standard DNN and an architecture-constrained DNN (c-DNN) based on problem-specific information. The results of the analysis showed that a c-DNN clearly outperforms a standard DNN in most of the cases considered. Moreover, it suggested that both adding constraints to the network architecture and having the lowest number of input features possible which are relevant to the problem addressed may have a positive impact in terms of reducing overfitting and getting better prediction results.
A Web Tool for Improving Case-Based Reasoning Model for Software Effort Estimation
Authors: Fellir Fadoua  , Nafil Khalid  , Idri Ali  , Chung Lawrence
Published: February 2025

Software effort estimation is a key factor for software development project success. Case-based reasoning (CBR), as a viable alternative to analogy methods, has often been used as a reference to calculate effort estimation. Albeit its conceptual simplicity, however, CBR-based effort estimation seems difficult and complex in reality, especially when enhancement on the estimate precision is highly desirable. For an effective and fast estimation, we present in this paper an innovative tool, automated-SEE (software effort estimation), for estimating software development effort using improved CBR. This tool enables the consideration of a comprehensive set of different types of requirements, including both functional requirements (FRs) and non-functional requirements (NFRs), and domain properties (DPs). Application of this tool to 36 (students') projects shows that the resulting software effort estimation is highly reliable.
Using Augmented Virtual Reality to Improve English Language Learning
Authors: Alaqsam Ahmad  , Selamat Ali  , Alias Rose Alinda  , Zakaria Nor Hidayati  , Puteh Fatimah  , Cheng Lim Kok  , Ahmad Mohammad Nazir
Published: February 2025

The enhancement of English language proficiency is a clear aim in many educational institutions around the world. One of the latest technology that has been adopted in education recently is Augmented Reality (AR) but still needs more consideration and investigation to insure its effectiveness in English language learning ELL. This paper highlights the most AR technologies that have been employed in ELL and views to what extent they have been useful and beneficial. Moreover, it points out to the limitations that would slowdown the adoption of AR in Education generally and English language learning particularly.
Mobile App Evolution Analysis Based on User Reviews
Authors: Li Xiaozhou  , Zhang Zheying  , Stefanidis Kostas
Published: February 2025

The user reviews of mobile apps are important assets that reflect the users' needs and complaints about particular apps regarding features, usability, and designs. From investigating the content of such reviews, the app developers can acquire useful information guiding the future maintenance and evolution work. Previous studies on opinion mining in mobile app reviews have provided various approaches to eliciting such critical information. A particular update of an app can provide changes to the app that result in users' reversed opinions, as well as, specific new complaints or praises. However, limited studies focus on eliciting the user opinions regarding a particular mobile app update, or the impact the update imposes. In this paper, we propose a method for systematically studying and analyzing the evolution of the users' opinions taking into consideration a set of mobile app updates. For doing so, we compare the topics appearing in the users' reviews before and after the updates. We also validate the method with an experiment on an existing mobile app.
Physiological Sensor System for the Detection of Human Moods Towards Internet of Robotic Things Applications
Authors: Fiorini Laura  , Semeraro Francesco  , Mancioppi Gianmaria  , Betti Stefano  , Santarelli Luca  , Cavallo Filippo
Published: February 2025

Internet of Robotic Things paradigm offers a concrete support to daily life. The pervasiveness of smart things, together with advances in cloud robotics, can help the smart systems to perceive and collect more information about the users and the environment. Often citizens have experienced “one-size-fits-all” approach, since the delivered service was not personalized, therefore resulting far from user's expectations. Hence, future smart agents, like robots, should produce personalized behaviours based on user emotions and moods in order to be more integrated into ordinary activities. In this work, we investigated the performances with unsupervised and supervised approaches to recognize three different moods elicited during a social interaction by means of a wearable system capable of measuring the Electrocardiogram, the ElectroDermal Activity and the Electroencephalographic signals. Particularly, the classification problem was analysed using three unsupervised (K-Mean, Self-Organizing Map and Hierarchical Clustering) and three supervised methods (Support Vector Machine, Decision Tree and k-nearest neighbour). The supervised algorithms reached an accuracy of 0.86 in the best case. The outcomes show that even in an unsupervised context the system is able to recognize the mood, reaching an accuracy equal to 0.76 in the best case.
Implementing the ArgQL Query Language
Authors: Zografistou Dimitra  , Flouris Giorgos  , Patkos Theodore  , Plexousakis Dimitris
Published: February 2025

Exploration and information identification constitute challenging research problems, with important applications in sensemaking over structured argumentative dialogues. In this paper, we present the implementation ArgQL, a high-level declarative query language, designed for querying dialogical data, structured in the principles of argumentation. We implement the language using an AIF-based representation and a translation of ArgQL into (complex) SPARQL queries. ArgQL provides a simple and intuitive way to query a structured dialogue using pure argumentative terminology.
Revisiting Computational Models of Argument Schemes: Classification, Annotation, Comparison
Authors: Visser Jacky  , Lawrence John  , Wagemans Jean  , Reed Chris
Published: February 2025

In this paper, we present an in-depth comparative analysis of two classifications of argument schemes: Walton's typology and Wagemans' Periodic Table of Arguments. We describe annotation guidelines for each classification and apply these to a corpus of arguments from the 2016 US presidential debates. In so doing, we achieve substantial inter-annotator agreement, and produce what, to the best of our knowledge, are the two largest and most reliably annotated corpora of argument schemes in dialogical argumentation publicly available. In describing the creation and comparison of these corpora, we discuss the strengths of each, with an eye towards both computational modelling and argument mining.
Project Debater
Authors: Slonim Noam
Published: February 2025

Project Debater is the first AI system that was shown to debate humans in a meaningful manner in a full live debate. Developing this system started in 2012, as the next AI Grand Challenge pursued by IBM Research, following the demonstration of Deep Blue in Chess in 1997, and Watson in Jeopardy! In 2011. The Project Debater system was demonstrated for the first time in San Francisco in June 2018, in two full live debates vs. expert human debaters, and correspondingly received massive media attention. This talk will present the challenges in developing this system, its current capabilities and present limitations, as well as how we envision its future.
Aggregating Alternative Extensions of Abstract Argumentation Frameworks: Preservation Results for Quota Rules
Authors: Chen Weiwei  , Endriss Ulle
Published: February 2025

When confronted with the same abstract argumentation framework, specifying a set of arguments and an attack-relation between them, different agents may disagree on which arguments to accept, i.e., they may choose different extensions. In the context of designing systems to support collective argumentation, we may then wish to aggregate such alternative extensions into a single extension that appropriately reflects the views of the group as a whole. Focusing on a conceptually and computationally simple family of aggregation rules, the quota rules, we analyse under what circumstances relevant properties of extensions shared by all extensions reported by the individual agents will be preserved under aggregation. The properties we consider are the classical properties of argumentation semantics, such as being a conflict-free, a complete, or a preferred extension. We show that, while for some properties there are quota rules that guarantee their preservation, for the more demanding properties it is impossible to do so in general.
Abstract and Concrete Decision Graphs for Choosing Extensions of Argumentation Frameworks
Authors: Dauphin Jérémie  , Cramer Marcos  , Van Der Torre Leendert
Published: February 2025

Most argumentation semantics allow for multiple extensions, which raises the question of how to choose among extensions. We propose to study this question as a decision problem. Inspired by decision trees commonly used in economics, we introduce the notion of a decision graph for deciding between the multiple extensions of a given AF in a given semantics. We distinguish between abstract decision graphs and concrete instantiations thereof. Inspired by the principle-based approach to argumentation, we formulate two principles that mappings from argumentation frameworks to decision graphs should satisfy, the principles of decision-graph directionality and that of directional decision-making. We then propose a concrete instantiation of decision graphs, which satisfies one of these principles. Finally, we discuss the potential for further research based on this novel methodology.
DABASCO: Generating AF, ADF, and ASPIC
Authors: Neugebauer Daniel
Published: February 2025

D-BAS [1] is an open-source web tool for dialog-based online argumentation among non-expert human users. We present DABASCO [2], a tool that utilizes D-BAS as a source of structured argumentation data and that allows to automatically translate D-BAS data into instances of abstract argumentation frameworks, abstract dialectical frameworks, and the ASPIC+ framework, thus offering the fully-automated generation of benchmark data from real discussions.
Collection of Resources and Evaluation of Customer Support Chatbot
Authors: Deksne Daiga  , Vasiļjevs Andrejs
Published: February 2025

In this study, we propose a practical approach to creating a chatbot on the basis of data accumulated by customer support operations. The selected use case is a typical representative of a chatbot application in customer service centers that want to improve their efficiency and raise customer satisfaction. We show how company support information and logs from support interactions can serve as source data for creating a customer support chatbot. The chatbot developed in this use case is targeted at Latvian-speaking users and demonstrates an implementation of Q&A functionality in the Latvian language. We also propose a simple evaluation metric for chatbot responses to natural language questions. As practical chatbots cannot be perfect in providing appropriate answers to all user questions, this metric can be used to assess the readiness of the chatbot for being released to real users. In our experiment, a chatbot with a score of 0.45 showed positive results in a user survey.
A Study on Contextual Influences on Automatic Playlist Continuation
Authors: Gatzioura Anna  , Sànchez-Marrè Miquel  , Jorge Alípio Mário
Published: February 2025

Recommender systems still mainly base their reasoning on pairwise interactions or information on individual entities, like item attributes or ratings, without properly evaluating the multiple dimensions of the recommendation problem. However, in many cases, like in music, items are rarely consumed in isolation, thus users rather need a set of items, selected to work well together, serving a specific purpose, while having some cognitive properties as a whole, related to their perception of quality and satisfaction, under given circumstances.
A New Algorithm for Speech Enhancement Based on Multivariate Empirical Mode Decomposition
Authors: Marti-Puig Pere  , Gallego-Jutglà Esteve  , Masferrer Gerard  , Solé-Casals Jordi
Published: February 2025

Nowadays many systems use speech as a way to interact with them. Therefore, machine learning systems are needed to perform various tasks on these recordings. But speech signals in a real environment are usually mixed with some other signals, such as noise. This may interfere with posterior signal processing applied to the signals. In this work, a new technique of data denoising is presented using Multivariate Empirical Mode Decomposition. To analyse the efficiency of the proposed technique we perform experiments with two microphones and four speakers. Different signal-to-noise ratios are checked in order to study the evolution of the improvement of the recovered data. An improvement of the analyzed data is obtained in all the cases, suggesting that this method could be used as a pre-enhancement step in speech processing algorithms.
Model of Decision Making Support for a Doctor in the Treatment of an Oncosurgical Patient in a Hospital
Authors: Udalov Yuri D.  , Vasilyeva Irina V.  , Gordienko Alexander V.  , Bakharev Sergey A.
Published: February 2025

At the present moment, there is no uniform approach to the decision to conduct a planned surgical intervention in case of oncosurgical pathology, with appropriate recommendations in different sources. Consequently, in modern medicine, there is a particular problem of an objective assessment of the patient's physical condition and a risk assessment of an upcoming surgical intervention for developing an individualized tactic of preparation for surgical treatment for each particular patient. The need for introduction of new approaches and technologies, as well as treatment regimens, that allow us to take a fresh look at the possibilities of solving the problems of predicting critical outcomes of planned operations, determined the purpose of our study. The result of our study was the design of a decision rule in the form of the Microsoft Access software, which allows to rank patients with oncological diseases by the probability of lethal outcome in surgery.
Towards an Evidence-Based Decision Support Tool for Management of Musculoskeletal Conditions
Authors: Yet Barbaros  , Marsh William  , Morrissey Dylan
Published: February 2025

Musculoskeletal (MSK) problems present an increasing burden for the healthcare sector, particularly in ageing populations. Advances in evidence are often slow to influence clinical decisions, suggesting decision support would be beneficial. We propose a Bayesian network (BN) for providing evidence-based decision support as it can explicitly represent domain knowledge as causal relations and allows both domain knowledge and clinical data to be combined to create a usable decision model. We make a preliminary evaluation of the model's performance.
Semantically Structured Web Form and Data Storage: A Generic Ontology-Driven Approach Applied to Breast Cancer
Authors: Sadki Fouad  , Bouaud Jacques  , Guézennec Gilles  , Séroussi Brigitte
Published: February 2025

Interoperability issues are common in biomedical informatics. Structured data acquisition modalities are of major importance since the data acquired need to be adequately structured and encoded to be automatically processed by dedicated systems. We have developed a web application that generates a semantically structured web form from an ontology. Beyond allowing for structured patient data acquisition, the application allows to save patient data in a triplestore storage solution, and to retrieve them. Thus, the application provides a solution for the user to edit, display, and delete patient data, when necessary. The method has been applied to generate a web form to acquire patient data from the breast cancer knowledge model structured as an OWL ontology of the guideline-based decision support system within the European-funded DESIREE project.
Common Data Models (CDMs) to Enhance International Big Data Analytics: A Diabetes Use Case to Compare Three CDMs
Authors: Liyanage Harshana  , Liaw Siaw-Teng  , Jonnagaddala Jitendra  , Hinton William  , de Lusignan Simon
Published: February 2025

Common data models (CDM) have enabled the simultaneous analysis of disparate and large data sources. A literature review identified three relevant CDMs: The Observational Medical Outcomes Partnership (OMOP) was the most cited; next the Sentinel; and then the Patient Centered Outcomes Research Institute (PCORI). We tested these three CDMs with fifteen pre-defined criteria for a diabetes cohort study use case, assessing the benefit (good diabetes control), risk (hypoglycaemia) and cost effectiveness of recently licenced medications. We found all three CDMs have a useful role in planning collaborative research and enhance analysis of data cross jurisdiction. However, the number of pre-defined criteria achieved by these three CDMs varied. OMOP met 14/15, Sentinel 13/15, and PCORI 10/15. None met the privacy level we specified, and most of the other gaps were clinical and cost outcome related data.
Phenotyping Obstructive Sleep Apnea Patients: A First Approach to Cluster Visualization
Authors: Ferreira-Santos Daniela  , Pereira Rodrigues Pedro
Published: February 2025

The varied phenotypes of obstructive sleep apnea (OSA) poses critical challenges, resulting in missed or delayed diagnosis. In this work, we applied k-modes, aiming to identify groups of OSA patients, based on demographic, physical examination, clinical history, and comorbidities characterization variables (n = 41) collected from 318 patients. Missing values were imputed with k-nearest neighbours (k-NN) and chi-square test was held. Thirteen variables were inserted in cluster analysis, resulting in three clusters. Cluster 1 were middle-aged men, while Cluster 3 were the oldest men and Cluster 2 mainly middle-aged women. Cluster 3 weighted the most, whereas Cluster 1 weighted the least. The same effect was described in increased neck circumference. The percentages of variables driving sleepiness, congestive heart failure, arrhythmias and pulmonary hypertension were very low (<20%) and OSA severity was more common in mild level. Our results suggest that it is possible to phenotype OSA patients in an objective way, as also, different (although not considered innovative) visualizations improve the recognition of this common sleep pathology.
Bringing Human Diversity into Design Processes Through Empathic Modelling
Authors: Lorentzen Lena  , Hedvall Per-Olof
Published: February 2025

Most products are developed while adapting to requirements from industrial production and logistics. To break that trend and design for people, we suggest focusing on those who put the strongest demands on the final solution. They cannot compensate for bad design solutions and are thereby, like sniffing dogs, guiding designers to meet peoples' needs. We always use a combination of empathic modelling and involvement of people with reduced functions to find new solutions to the problems a product is supposed to solve. We have used this method in the teaching of Universal design at different universities for more than ten years. The students find the exercises to be a very entertaining eye-opener leading to development of empathy for human diversity all while the level of innovation in their design work increase. To constantly make design students understand barriers that can occur due to bad design solutions we utilize a toolbox simulating different kinds of functional ability. It also includes a handbook that describes workshops, evaluation methods and design processes that can be performed using the tools. The goal is to guide efficient, innovative and inclusive design processes. By simulating diversity among people, the designer can interpret the needs of different users and use that as a starting point and for evaluating design solutions during the creative process.
Universal Design in Architectural Education – Community Liaison on ‘Live Projects’
Authors: Busby Kevin  , Harrison Jim
Published: February 2025

The infusion of Universal Design principles into existing courses in architecture should become evident in any project work undertaken. ‘Live project’ is a term used to describe projects that engage the academic world with real-world groups/organizations. CCAE sees such projects as valuable exercises in a student's education, particularly, the practical experience of interaction with ‘user-experts.’ In 2016 Cork County Council approached CCAE with a proposal to promote age-friendly housing as part of their age-friendly initiative. CCAE developed this into a ‘live project’ for Year 2 architecture students, continuing the integration of UD into the curriculum. This helps students to identify the negative disabling aspects of ageing and show UD principles can be seen as commonplace. For their part, the County Council were able to expand their own thinking, availing of the less constrained ideas that students brought to their schemes. An approach to achieving the adoption of UD is to consider the Vitruvian definition of architecture as having ‘commodity, firmness and delight’. From this, the aesthetic integration of features to benefit users of limited ability can be achieved without stigmatising anyone as being old or disabled. Now in its second year the project is being run in West Cork. The chosen site in Bantry town centre, has interesting challenges for the students to incorporate UD principles. This paper will present imaginative but viable projects as examples of student' responses to the challenges of designing housing solutions and will report on their ability to integrate age-friendly features at different scales.
The Development of a Methodology for Contextual User Research in Healthcare Design Projects
Authors: Healion Donal  , O'Dowd Enda  , Russell Sam
Published: February 2025

The impact of human factors in the usage of medical devices and delivery of healthcare is increasingly being recognized as a significant contributor to patient experience and safety. This paper presents a methodology for undertaking contextual user research during healthcare design projects (in home, primary or acute care settings) by which all relevant human factors of a procedure can be recorded, documented and analyzed. An innovative method of graphically representing the results of this analysis is proposed which visualizes the interactions and interdependencies between all stakeholders and artefacts involved in a procedure and the environment in which it takes place. The proposed methodology is intended to assist researchers, designers, architects and healthcare professionals during the research phase of a healthcare design project to reveal user needs, identify potential risks, provide documentation for regulatory adherence and inform the development of a comprehensive and inclusive design brief. The paper presents the context and development of this systematic process, which draws on empirical and theoretical methodologies, a studio based pedagogy, and the experience of delivering real-world educational design projects in partnership with healthcare clinicians and medical device companies. It also highlights the capacity for this form of learning to align with Universal Design for Learning (UDL) principles. The application of the methodology has the ability to extract key environmental, user and human factors insights. Most importantly, these insights can inform the design process, positively impact on patient experience and safety through improvements in device development and care delivery, and enable the creation of more inclusive and accessible healthcare solutions.
Feedback from Digital Systems Used in Higher Education: An Inquiry into Triggered Emotions
Authors: Saplacan Diana  , Herstad Jo  , Pajalic Zada
Published: February 2025

We illustrate in this paper some of the negative emotions experienced by students when interacting with digital systems in learning situations, where there is a lack of feedback, or the interaction with digital systems is faulty. The emotions are classified here as feelings of: neglection, frustration, uncertainty, need for confirmation, and discomfort. We proposed thereafter two solutions. The first solution focuses on the design of digital feedback from a system which should arouse positive emotions. We discuss this in relation to universal design principle 5, tolerance for error. The second solution we propose is to provide feedback mechanisms for user feedback (user input). This may be considered when the first solution is not possible.
A New Formula and New Constants Hidden After the Euler's Formula and the Euler's Constant
Authors: Chen Wenwei  , Chen Sheng
Published: February 2025

In this paper, the authors propose a new formula π=½eθ, in which the new constant θ is a real number. The new formula is a perfect supplement the Euler's formula eπi=−1. These two formulas together reveal the completely relationship between π and e. Constant θ=1+γ+2μ, γ is the Euler's constant, constant. We call μ and θ new constants hidden after the Euler's constant γ.
Prediction of Food Safety Network of Public Opinion Hot-Degree Based on Improved Entropy Method and Markov
Authors: Hong Xiaojuan  , Zhang Cong  , Huang Weidong  , Hong Wei
Published: February 2025

According to the short life cycle of food safety of public opinion and the disciplinary development curve, it is feasible for prediction of public opinion hot-degree to use improved entropy and Markov model. The paper selects “dirty porterhouse of one well-known catering enterprise” food safety public event in 2016, setting up comprehensive evaluation index system by AHP: the media, net citizen and government as the criterion level. Index weight can be calculated with improved entropy method. The state transition matrix will be constructed and food safety of public opinion's change interval will also be predicted by Markov. The results show that the comprehensive construction of the index system and the improved entropy weight method indeed improve the accuracy of the Markov model, and effectively realize the prediction of food safety public opinion finally.
Improving the Perception of Chemistry in Higher Education Programs Through Many-Valued Empirical Machines
Authors: Vicente Henrique  , Figueiredo Margarida  , Dias Almeida  , Marques José  , Araújo Isabel  , Maia Nuno  , Ribeiro Jorge  , Neves José
Published: February 2025

The inclusion of the chemistry field of study in higher education science and technology curricula aims to develop professionals who are able to analyze and solve multidisciplinary problems in a sustainable and correct way. Attending students to assess the role of chemistry in their education is critical to increasing success and improving their future professional practice. This article presents a Many-Valued Empirical Machine designed to capture Students' Perception of Chemistry in Higher Education Programs. The applied problem-solving method is based on a symbolic/sub-symbolic line of logical formalisms that articulate with an Artificial Neural Network approach to computing, being grounded on a view to knowledge representation and argumentation that considers not only the data entropic states but also its inherent Predicative Vagueness.
Multicast Routing Algorithm for Ad hoc Networks Based on Cognitive Radio
Authors: Zhang Fan  , Xu Xinyan  , Xie Liguo  , Yuan Qi
Published: February 2025

Mobile Ad hoc networks (MANETs) have the characteristics of distribution, self-organization and mobility. PUMA, as a mesh-based multicast routing protocol for MANET, has been proved to perform better than other comparable multicast algorithms because of its effectiveness and robustness. Recently, cognitive radio technology has been applied to solve the scarcity problem of radio spectrum resources. In this paper, we combine PUMA with cognitive radio technology and propose a novel multicast routing algorithm, which is named as CR-PUMA for MANET. In CR-PUMA, node detects the available channels and selects a suitable one to send data packets to its next-hop neighbor. A cross-layer approach, which goes across the physical layer, link layer and network layer, is employed in our algorithm. Simulation experiments show that CR-PUMA attains a superior performance in terms of packet delivery ratio and throughput.
Post-Operative Stoma Management Based on 3D Image Reconstruction and Pattern Recognition
Authors: Makhsous Sep  , Zielinski Aaron  , Mamishev Alexander
Published: February 2025

A stoma appliance is an external to the body bag for the collection of intestinal output via an aperture in the patient's abdominal wall. The bag is attached to the skin using a wafer, and many stomas have irregular shapes, making a manual wafer fitting very difficult. Better post-operative wafer fitting has been identified as a key area for stoma care improvement. The ostomate (or the ostomate's caregiver) must know the diameter of the stoma to fit the appliance accurately. A cutting error of as little as 2 mm can result in serious complications, including damaged blood vessels or profuse, recurrent bleeding. A novel approach is presented for the design of a visual measurement assistive system called the Individualized Wafer Fitting System (WFS). The system includes a 3D structured light scanner and associated software. The image processing algorithm and smartphone application interact with the sensor and the ostomate. Once the measurement is complete, the outline of the stoma is sent to a printer, which produces the template for cutting the individualized wafer. The system was tested on artificially made stomas as well as on a human subject for a proof of concept. The results show an error of 5% in cutting wafers customized for simple stomas.