bluuebunny/crossref_metadata_2025 huggingface dataset:
Cultural Problems Associated with the Implementation of eHealth
Authors: Mandirola Brieux H.-F.  , Benitez S.  , Otero C.  , Luna D.  , Masud J.H.B.  , Marcelo A.  , Househ M.  , Hullin C.  , Villalba C.  , Indarte S.  , Guillen S.  , Otero P.  , Campos F.  , Baum A.  , Gonzalez Bernaldo de Quirós F.
Published: February 2025

A major hurdle in eHealth implementation is that it is difficult to put into practice. In this study, the primary aim was to identify the main barrier associated with implementing eHealth. This study surveyed IMIA members from May to November 2015. From the results, it is clear that medical professionals were recording most of their data by hand. This paper culture is a paradigm that is difficult to break. Cultural factors are the primary barrier in eHealth implementation.
Comparing the Human Papillomavirus Vaccination Opinions Trends from Different Twitter User Groups with a Machine Learning Based System and Semiparametric Nonlinear Regression
Authors: Du Jingcheng  , Huang Jing  , Duan Rui  , Chen Yong  , Tao Cui
Published: February 2025

HPV vaccination refusal is a serious public health issue. Opinions on Twitter have are influential to potential consumers on vaccination behaviours. Public opinions toward HPV vaccination were extracted from Twitter by leveraging machine learning models. We used semiparametric nonlinear regression models to study the association between people's public opinions on HPV vaccination at Twitter and the number of followers. Trend patterns of opinions from Twitter users groups with different number of followers were identified and compared.
Patient-Clinician Co-Design Co-Participation in Design of an App for Rheumatoid Arthritis Management via Telehealth Yields an App with High Usability and Acceptance
Authors: Grainger Rebecca  , Townsley Hermaleigh  , Langlotz Tobias  , Taylor William
Published: February 2025

People with chronic conditions like rheumatoid arthritis (RA) self-manage on a day to day basis. They may be able to assess disease activity and communicate this via an app to their healthcare team to enable clinical review for medical management at the most appropriate times. This work describes the succesful co-design of a patient-held app for monitoring and communication of RA disease activity.
Three Dimensional Virtual Planning Through Cone Beam Computed Tomography for Surgical Guidance Production
Authors: Antunes Kaline Thumé  , Cordeiro d'Ornellas Marcos  , Noedel Daniani Deliberalli  , Dotto Gustavo Nogara  , Ghres Renésio Armindo
Published: February 2025

This paper describes a methodology through digital planning and computed tomography for the purpose of making surgical guides for insertion of skeletal anchorage devices in dentistry. Mini-orthodontic screws for anchorage are inserted virtually in the region of interest located between the teeth of the second premolar and first molar bilaterally. DICOM images were converted to STL format and overlaid by scanning the upper plaster model in order to plan the surgical guide virtually.
Delivering Digital Drugs: An Exploratory Study of the Digitalisation of Supply and Use of Medicines
Authors: Cornford Tony  , Lichtner Valentina  , Dickson Jane  , Hibberd Ralph  , Klecun Ela  , Venters Will  , Franklin Bryony Dean
Published: February 2025

Medicines' supply and use is incresingly reliant on digital means and information. This poster presents exploratory research over five episodes of digitalisation of medicines across the supply network. We ‘follow the drug’ through this emerging field, providing an initial map of this new territory.
Deep Diabetologist: Learning to Prescribe Hypoglycemic Medications with Recurrent Neural Networks
Authors: Mei Jing  , Zhao Shiwan  , Jin Feng  , Zhang Lingxiao  , Liu Haifeng  , Li Xiang  , Xie Guotong  , Li Xuejun  , Xu Meilin
Published: February 2025

In healthcare, applying deep learning models to electronic health records (EHRs) has drawn considerable attention. This sequential nature of EHR data make them wellmatched for the power of Recurrent Neural Network (RNN). In this poster, we propose “Deep Diabetologist” – using RNNs for EHR sequential data modeling to provide personalized hypoglycemic medication prediction for diabetic patients. Our results demonstrate improved performance compared with a baseline classifier using logistic regression.
Estimating Sample Size for a Feasibility Study of Computer-Assisted Input Support to EDC
Authors: Nishimoto Naoki  , Tamura Naomi  , Kunikata Jun  , Akahori Sumiko  , Sogo Tomoaki  , Tani Yuma  , Yokoi Hideto
Published: February 2025

The aim of this study was to to estimate the sample size for the assumed feasibility study of the computer-assisted input support on a clinica trial. More than 1,500 observations were required for the feasibility study with assumed settings. Further study was required for more efficient research design.
Automated Report Generation for Research Data Repositories: From i2b2 to PDF
Authors: Thiemann Volker S.  , Xu Tingyan  , Röhrig Rainer  , Majeed Raphael W.
Published: February 2025

We developed an automated toolchain to generate reports of i2b2 data. It is based on free open source software and runs on a Java Application Server. It is sucessfully used in an ED registry project. The solution is highly configurable and portable to other projects based on i2b2 or compatible factual data sources.
Usability Study of RSNA Radiology Reporting Template Library
Authors: Hong Yi  , Zhu Ying  , Bozkurt Selen  , Zhang Jin  , Kahn Jr. Charles E.
Published: February 2025

This study provides insights that could help to improve the Radiological Society of North America (RSNA) Reporting Template Digital Library, based on a usability evaluation. The results show that most users have been satisfied with the website. The general comments for the library are positive, although the participants suggested quite a few areas to improve. About 40% are returning visitors which means people often come back to the website.
Building Interoperable FHIR-Based Vocabulary Mapping Services: A Case Study of OHDSI Vocabularies and Mappings
Authors: Jiang Guoqian  , Kiefer Richard  , Prud'hommeaux Eric  , Solbrig Harold R.
Published: February 2025

The OHDSI Common Data Model (CDM) is a deep information model, in which its vocabulary component plays a critical role in enabling consistent coding and query of clinical data. The objective of the study is to create methods and tools to expose the OHDSI vocabularies and mappings as the vocabulary mapping services using two HL7 FHIR core terminology resources ConceptMap and ValueSet. We discuss the benefits and challenges in building the FHIR-based terminology services.
Using SNOMED Distance to Measure Semantic Similarity of Clinical Trials
Authors: Wei Duo (Helen)  , Fu Gang
Published: February 2025

The objective of this research is to propose to develop an innovative distance function, called SNOMED distance, which captures the nature of the semantic distance within the topological structure of SNOMED, to identify semantic similarity between clinical trials.
Accessibility of Mobile Devices for Visually Impaired Users: An Evaluation of the Screen-Reader VoiceOver
Authors: Smaradottir Berglind  , Håland Jarle  , Martinez Santiago
Published: February 2025

A mobile device's touchscreen allows users to use a choreography of hand gestures to interact with the user interface. A screen reader on a mobile device is designed to support the interaction of visually disabled users while using gestures. This paper presents an evaluation of VoiceOver, a screen reader in Apple Inc. products. The evaluation was a part of the research project “Visually impaired users touching the screen – a user evaluation of assistive technology”.
The Future of Mobile Usability, Workflow and Safety Testing
Authors: Borycki Elizabeth  , Senathirajah Yalini  , Kushniruk Andre W.
Published: February 2025

In this paper, the authors outline a vision for the future of mobile usability, workflow and safety testing. The authors argue for the use of glasses that can audio and video record usability, workflow and safety data. Here, citizens, patients and health professionals would become collectors of study data as they use mobile devices and software to support healthcare in the real world. This has become quite feasible with the introduction of low-cost glasses and software applications that allow for the uploading of data for additional analysis by researchers and evaluators of mobile technologies.
Guidance Through Use: Value as a Pathfinder in e-Health Services Implementation
Authors: Lindenfalk Bertil  , Vimarlund Vivian
Published: February 2025

The lack of awareness and confidence in eHealth solutions among certain stakeholders creates a barrier for the implementation of e-Health services. The aim of this paper is to explore issues that promote the development and implementation of patient-centered care services for the elderly. An exploratory case study approach is applied to a e-Health monitoring service that was developed and piloted in 38 homes for the elderly in Sweden and the Netherlands. The unit of analysis, concept of ‘value-in-use’, was used in order to determine how pilot participants felt about a service of this kind benefiting them the most. The findings were then translated into actionable considerations for implementing organizations. The results indicate a need for active participation, technical support infrastructure, mobility demands, and an extension of the concept of trust in e-Health services. The knowledge presented in the study is important for decisions makers, public organization strategists, and policy writers.
Health Smart Homes: New Challenges
Authors: Maeder Anthony J.  , Williams Patricia A.H.
Published: February 2025

Health Smart Homes provide various forms of assisted living support, by monitoring the activities and health status of their occupants to generate flows of information and sometimes interventions involving the occupants and their careers. Technologies of varying complexity must be combined to produce the underlying Health Smart Home system, and processing of the resulting data may require methods of varying sophistication. These aspects have been well studied, but no widely-adopted approaches for practical implementation of systems or systematic processing of data have been developed. Also, the integration of Health Smart Home services with the overall health care system has not been regularized. This paper identifies and categorizes the emerging high-level challenges beyond those in the basic technical and algorithmic spaces. These challenges will influence future directions for Health Smart Homes and their wider adoption and integration with health systems.
Analysis of HL7 EHRS Functional Model and Suggested Applications in China
Authors: Meng Haibin  , Mao Huajian  , Chi Chenyang  , Zhao Dongsheng
Published: February 2025

HL7 electronic health record system (EHRS) functional model is an international standard which gives scientific instructions for EHRS design and implementation. It has the features of technological irrelevancy, requirement hierarchy and functional comprehensiveness. It is composed of overarching, care provision, care provision support, administrative support, population health support, record infrastructure and trusted infrastructure. This paper describes the functionalities of each section in detail. To apply this functional model, the history of Chinese health information system construction is reviewed and the weakness is analyzed as a basis. Then this paper gives suggestions on the construction of health information system in China: to enhance the functionality of healthcare systems, to enhance the information network function of regional health information network (RHIN), and to redesign the trust architecture of RHIN.
Design and Evaluation on the Mobile Application of Transcutaneous Electrical Nerve Stimulation (TENS)
Authors: Cheng Ching-Lung  , Lee Li-Hui  , Cheng Yu-Ting
Published: February 2025

This study aims to design a transcutaneous electrical nerve stimulation Application (TENS App) according to the suggestions from potential users. To the best of our knowledge, this is the first App including meridian and acupoints for TENS. After its development, there are eight participants recruited for evaluating the usability. Despite two out of eight users reporting that the typical TENS system requires lower cost and has better functionality than TENS App, the results show that almost seventy percent of participants have a better perception of TENS App on price, functionality, convenience, operational ability, and quality. However, participants still reported concerns about the safety issue of adopting TENS App. Therefore, for people who are the first time or unfamiliar with TENS App, instructions from occupational or physical therapists are recommended. We conclude that by using TENS App, users can not only use the portable electrotherapy devices at anyplace, but also reduce their outpatient visits.
A Validated Risk Model for 30-Day Readmission for Heart Failure
Authors: Mahajan Satish M.  , Burman Prabir  , Newton Ana  , Heidenreich Paul A.
Published: February 2025

One of the goals of the Precision Medicine Initiative launched in the United States in 2016 is to use innovative tools and sources in data science. We realized this goal by implementing a use case that identified patients with heart failure at Veterans Health Administration using data from the Electronic Health Records from multiple health domains between 2005 and 2013. We applied a regularized logistic regression model and predicted 30-day readmission risk for 1210 unique patients. Our validation cohort resulted in a C-statistic of 0.84. Our top predictors of readmission were prior diagnosis of heart failure, vascular and renal diseases, and malnutrition as comorbidities, compliance with outpatient follow-up, and low socioeconomic status. This validated risk prediction scheme delivered better performance than the published models so far (C-Statistics: 0.69). It can be used to stratify patients for readmission and to aid clinicians in delivering precise health interventions.
CrowdMapping: A Crowdsourcing-Based Terminology Mapping Method for Medical Data Standardization
Authors: Mao Huajian  , Chi Chenyang  , Huang Boyu  , Meng Haibin  , Yu Jinghui  , Zhao Dongsheng
Published: February 2025

Standardized terminology is the prerequisite of data exchange in analysis of clinical processes. However, data from different electronic health record systems are based on idiosyncratic terminology systems, especially when the data is from different hospitals and healthcare organizations. Terminology standardization is necessary for the medical data analysis. We propose a crowdsourcing-based terminology mapping method, CrowdMapping, to standardize the terminology in medical data. CrowdMapping uses a confidential model to determine how terminologies are mapped to a standard system, like ICD-10. The model uses mappings from different health care organizations and evaluates the diversity of the mapping to determine a more sophisticated mapping rule. Further, the CrowdMapping model enables users to rate the mapping result and interact with the model evaluation. CrowdMapping is a work-in-progress system, we present initial results mapping terminologies.
Can Sonification Become a Useful Tool for Medical Data Representation?
Authors: Mihalas George I.  , Andor Minodora  , Tudor Anca  , Paralescu Sorin
Published: February 2025

This ‘vision’ paper refers to sonification – a novel method to represent data by sounds. A short theoretical background comprises the main features to attach sound to a set of data – how to map the correspondence between the sound parameters (pitch, duration) and the initial set of data. The classification of sonification methods is followed by a description of sound display tools – tempolenses and artifacts (saccadic display or loudness variations). The Results section comprises examples of sonification performed by our team: heart rate (HR), ECG signals, HR variations during exercise, including warning procedures. The procedure to evaluate the discriminant power of various sonification algorithms is then described. As a ‘vision’ paper, the most important part is not represented by the results, but the potential future developments, presented in the Discussion section, which starts with a critical view of the present state and presents future potential applications of sonification in medicine.
Longitudinal Changes in Risk Stratification for a Managed Population
Authors: Sheets Lincoln  , Popescu Mihail  , Lyttle Kayson  , Cho Soo-Yeon  , Parker Jerry C.
Published: February 2025

The LIGHT2project managed the care of approximately 10,000 Medicare (primarily elderly) and Medicaid (low income) patients between 2013 and 2015. Risk tiers based on chronic disease diagnoses and recent healthcare utilization were strongly predictive of future healthcare utilization, and the authors expected that the members of an aging and well-insured population would gradually rise in risk of healthcare utilization over the course of three years. Various analytic techniques were used to characterize the members of higher risk tiers. However, retrospective cohort analysis and simple data visualization discovered the tendency of patients in lower initial risk tiers to remain healthy, and the tendency of patients in higher initial risk tiers to improve. In a time frame of three years, this return to stability was a more important influence on healthcare utilization than risk or aging.
Automating the Identification of Patient Safety Incident Reports Using Multi-Label Classification
Authors: Wang Ying  , Coiera Enrico  , Runciman William  , Magrabi Farah
Published: February 2025

Automated identification provides an efficient way to categorize patient safety incidents. Previous studies have focused on identifying single incident types relating to a specific patient safety problem, e.g., clinical handover. In reality, there are multiple types of incidents reflecting the breadth of patient safety problems and a single report may describe multiple problems, i.e., it can be assigned multiple type labels. This study evaluated the abilty of multi-label classification methods to identify multiple incident types in single reports. Three multi-label methods were evaluated: binary relevance, classifier chains and ensemble of classifier chains. We found that an ensemble of classifier chains was the most effective method using binary Support Vector Machines with radial basis function kernel and bag-of-words feature extraction, performing equally well on balanced and stratified datasets, (F-score: 73.7% vs. 74.7%). Classifiers were able to identify six common incident types: falls, medications, pressure injury, aggression, documentation problems and others.
Applying Risk Models on Patients with Unknown Predictor Values: An Incremental Learning Approach
Authors: Xu Enliang  , Li Xiang  , Mei Jing  , Zhao Shiwan  , Hu Gang  , Xia Eryu  , Liu Haifeng  , Xie Guotong  , Xu Meilin  , Li Xuejun
Published: February 2025

In clinical practice, many patients may have unknown or missing values for some predictors, causing that the developed risk models cannot be directly applied on these patients. In this paper, we propose an incremental learning approach to apply a developed risk model on new patients with unknown predictor values, which imputes a patient's unknown values based on his/her k-nearest neighbors (k-NN) from the incremental population. We perform a real world case study by developing a risk prediction model of stroke for patients with Type 2 diabetes mellitus from EHR data, and incrementally applying the risk model on a sequence of new patients. The experimental results show that our risk prediction model of stroke has good prediction performance. And the k-nearest neighbors based incremental learning approach for data imputation can gradually increase the prediction performance when the model is applied on new patients.
Semantic Relations in Compound Nouns: Perspectives from Inter-Annotator Agreement
Authors: Yadav Prabha  , Jezek Elisabetta  , Bouillon Pierrette  , Callahan Tiffany J.  , Bada Michael  , Hunter Lawrence E.  , Cohen K. Bretonnel
Published: February 2025

Semantic relations have been studied for decades without yet reaching consensus on the set of these relations. However, biomedical language processing and ontologies rely on these relations, so it is important to be able to evaluate their suitability. In this paper we examine the role of inter-annotator agreement in choosing between competing proposals regarding the set of such relations. The experiments consisted of labeling the semantic relations between two elements of noun-noun compounds (e.g. cell migration). Two judges annotated a dataset of terms from the biomedical domain using two competing sets of relations and analyzed the inter-annotator agreement. With no training and little documentation, agreement on this task was fairly high and disagreements were consistent. The results support the utility of the relation-based approach to semantic representation.
“Hybrid Topics” – Facilitating the Interpretation of Topics Through the Addition of MeSH Descriptors to Bags of Words
Authors: Yu Zhiguo  , Nguyen Thang  , Dhombres Ferdinand  , Johnson Todd  , Bodenreider Olivier
Published: February 2025

Extracting and understanding information, themes and relationships from large collections of documents is an important task for biomedical researchers. Latent Dirichlet Allocation is an unsupervised topic modeling technique using the bag-of-words assumption that has been applied extensively to unveil hidden thematic information within large sets of documents. In this paper, we added MeSH descriptors to the bag-of-words assumption to generate ‘hybrid topics’, which are mixed vectors of words and descriptors. We evaluated this approach on the quality and interpretability of topics in both a general corpus and a specialized corpus. Our results demonstrated that the coherence of ‘hybrid topics’ is higher than that of regular bag-of-words topics in the specialized corpus. We also found that the proportion of topics that are not associated with MeSH descriptors is higher in the specialized corpus than in the general corpus.
Design and Construction for Community Health Service Precision Fund Appropriation System Based on Performance Management
Authors: Gao Xing  , He Yao  , Hu Hongpu
Published: February 2025

Allowing for the differences in economy development, informatization degree and characteristic of population served and so on among different community health service organizations, community health service precision fund appropriation system based on performance management is designed, which can provide support for the government to appropriate financial funds scientifically and rationally for primary care. The system has the characteristic of flexibility and practicability, in which there are five subsystems including data acquisition, parameter setting, fund appropriation, statistical analysis system and user management.
The Australian Health Informatics Competencies Framework and Its Role in the Certified Health Informatician Australasia (CHIA) Program
Authors: Martin-Sanchez Fernando  , Rowlands David  , Schaper Louise  , Hansen David
Published: February 2025

The Certified Health Informatician Australasia (CHIA) program consists of an online exam, which aims to test whether a candidate has the knowledge and skills that are identified in the competencies framework to perform as a health informatics professional. The CHIA Health Informatics Competencies Framework provides the context in which the questions for the exam have been developed. The core competencies for health informatics that are tested in the exam have been developed with reference to similar programs by the American Medical Informatics Association, the International Medical Informatics Association and COACH, Canada's Health Informatics Association, and builds on the previous work done by the Australian Health Informatics Education Council. This paper shows how the development of this competency framework is helping to raise the profile of health informaticians in Australasia, contributing to a wider recognition of the profession, and defining more clearly the body of knowledge underpinning this discipline. This framework can also be used as a set of guidelines for recruiting purposes, definitions of career pathways, or the design of educational and training activities. We discuss here the current status of the program, its resultsandprospectsfor the future.
Peer Review of Clinical Information Models: A Web 2.0 Crowdsourced Approach
Authors: Leslie Heather  , Ljosland Bakke Silje
Published: February 2025

Over the past 8 years the openEHR Clinical Model program has been developing a Web 2.0 approach and tooling to support the development, review and governance of atomic clincial information models, known as archetypes. This paper describes the background and review process, and provides a practical example where cross standards organisation collaboration resulted in jointly agreed clinical content which was subsequently represented in different implementation formalisms that were effectively semantically aligned. The discussion and conclusions highlight some of the socio-technical benefits and challenges facing organisations who seek to govern automic clinical information models in a global and collaborative online community.
Surveying a New Multi-Institution Clinical Data Research Network
Authors: Rosenman Marc  , Madden Margaret  , Oh Elissa  , Goel Satyender  , Kho Abel
Published: February 2025

Cultivated by the Patient-Centered Outcomes Research Network (PCORnet), thirteen regional clinical data research networks (CDRNs) are taking shape across the U.S. The PCORnet common data model was carefully planned, and the data marts assembled by the more than 80 data-contributing institutions (nodes) are undergoing, in 2016–2017, a series of data characterization cycles. PCORnet will adjudge each node's—and thereby, in a significant way, each CDRN's—readiness or unreadiness for multi-institution research. Certifying each node's quality and fidelity is of course essential. But in understanding network readiness there is an additional, vital dimension—one that has received too little attention. It is the development of knowledge about the nature of a CDRN's data, in its federated sense. With visualizations, how might one grasp the meta-data of a CDRN? We outline an approach that builds upon the HealthLNK Data Repository, a forerunner to the Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN) CDRN.
Designing a Mobile Health Application Prototype for the Management of Interstitial Cystitis/Painful Bladder Syndrome
Authors: Griffith Janessa
Published: February 2025

The design of an early mobile health application (app) prototype to manage interstitial cystitis/painful bladder syndrome, a chronic condition characterized by recurrent pain/discomfort in the bladder and pelvic floor, is described. The purpose of this app prototype is to help people who have IC/PBS manage and learn what triggers their symptoms. Another aim of this research was to provide an example of how sex and gender could be included into the design of a health information system. Based on a literature search of common symptoms and challenges faced by people living with IC/PBS, the researcher created an app prototype design including many features: resources for relaxation, mental health, intimacy, pregnancy, and daily life; reminders for appointments, and medication; logs for diet, activity, sleep, pain, menstruation; and a link to a public washroom locator. This prototype will later undergo usability and content evaluation.
What Have We Learned from the CSIRO National NBN Telehealth Trial?
Authors: Celler Branko  , Varnfield Marlien  , Jayasena Rajiv
Published: February 2025

The CSIRO National NBN Telehealth Trial investigated the effects of introducing at home telemonitoring of vital signs for the management of a heterogeneous group of chronically ill patients. Patients suffering from a wide range of chronic conditions who were frequently admitted to hospital, were selected from nominated hospital lists. The impact of telemonitoring was analysed using a wide range of health and wellbeing outcomes as well as numerous health economic metrics derived from Medicare Medical Benefits Scheme (MBS) and Pharmaceutical Benefits Scheme (PBS) data and Hospital Health Roundtable data. Data was also recorded from the telemonitoring system used in the trial, and the administration of questionnaires. The impact of this intervention on the patients, carers and clinicians involved in their care was quantitatively and qualitatively analysed and documented. There were no significant differences between test and control patients at baseline. Test patients were monitored for an average of 276 days with 75% of patients monitored for more than 6 months. Test patients 1 year after the start of their intervention showed a 46.3% reduction in rate of predicted medical expenditure, a 25.5% reduction in the rate of predicted pharmaceutical expenditure, a 53.2% reduction in the rate of predicted unscheduled admission to hospital, a 67.9% reduction in the predicted rate of LOS when admitted to hospital, and a reduction in mortality of between 41.3% and 44.5% relative to control patients. Control patients did not demonstrate any significant change in their predicted trajectory for any of the above variables. In addition, this project reports on the effect of workplace culture and capacity for innovation and organizational change management in successfully integrating a new model of care with long established service models.
Telehealth in the Elderly with Chronic Heart Failure: What Is the Evidence?
Authors: Clark Robyn A.
Published: February 2025

Background: In a series of studies over the past decade we have measured the effectiveness of telehealth for elderly heart failure patients (>70 years) on rehospitalisation, mortality, adherence, satisfaction, cost effectiveness, health related quality of life, age and types of technology.
Systems to Harness Digital Footprint to Elucidate and Facilitate Ageing in Place
Authors: Knight Alissa  , Fouyaxis John  , Jarrad Geoff  , Beski Kinga  , Cho Gerald  , Bidargaddi Niranjan
Published: February 2025

The rapid ageing of the population is a worldwide inexorable demographic transformation. At a time of immense social, political and economic change, the growing elderly population is at the forefront of global burden, placing an increasing strain on the federal, state, and local budgets. Many public policy responses to the impending ageing epidemic have begun, particularly with regards to dementia prevention and quality of life for older adults. However, to date, the fruition of such efforts remains to be discovered. Indeed, there is a need to find more novel and multifaceted ways of understanding the fragmentary changes and underlying mechanisms in the biopsychosocial contexts of ageing. Discovering better ways to measure these intricate domains will create better insight into how to improve clinical and public health information systems for the development of more personalisation support and services across the continuum of aged care. Technology now permeates all aspects of our everyday living. Digital footprints are data arising as a by-product of interactions we do as part of everyday living. The digital traces we live behind, be it on internet, social media, on mobile phone apps, as well as in health records (EHRs) could be used to infer how we behave and interact with environment, and how we feel in different situations. Commercial sector has very successfully used these footprints in the advertisement and marketing space. This type of information may provide clinicians with an unobtrusive way of monitoring older adults in their daily living, and provide an alternative means to traditional self-report and expert-rated assessment. In this paper we present two innovative digital footprint applications, Actionable Intime Insights and the SAIL Mobile app, which aim to facilitate “Ageing in Place” through adaptive, dynamic, early intervention strategies. These systems are devised to unveil contextual indicators of how a person is functioning mentally, socially, behaviourally and physically in their own environment, as to as assist those with chronic conditions better self-manage by facilitating assistance with care and medication needs just in time.
SCREEN-DR
Authors: Pedrosa Micael  , Silva Jorge Miguel  , Matos Sérgio  , Costa Carlos
Published: February 2025

Diabetic Retinopathy (DR) is a common complication of diabetes that may lead to blindness if not treated. However, since DR evolves without any symptoms in the initial stages, early detection and treatment can only be achieved through routine checks. This article presents the collaborative platform of the SCREEN-DR project that promotes partnership between physicians and researchers in the scope of a regional DR screening program. The role of researchers is to create classification algorithms to evaluate image quality, discard non-pathological cases, locate possible lesions and grade DR severity. Physicians are responsible for annotating datasets, including the visual delineation of lesions. The collaborative platform collects the studies, indexes the images metadata, and manages the creation of datasets and the respective annotation process. An advanced searching mechanism supports multimodal queries over annotated datasets and exporting of results for feeding artificial intelligence algorithms.
Implementation of a New Traceability Process for Breast Milk Feeding
Authors: Daus Mariana Y.  , Maydana Thelma G.  , Rizzato Lede Daniel A.  , Luna Daniel R.
Published: February 2025

Many newborns at the neonatal intensive care unit are unable to feed themselves, and receive human milk through enteric nutrition devices such as orogastric or nasogastric probes. The mothers extract their milk, and the nursing staff is responsible for the fractionation, storage and administration when prescribed by physicians. It is very important to remind that it is a bodily fluid that carries the risk of disease transmission if misused. Health information technologies can enhance patient safety by avoiding preventable adverse events. Barcoding technology could track every step of the milk manipulation. Many processes must be addressed to implement it. Our goal is to explain our planning and implementation process in an academic tertiary hospital.
Combining the Generic Entity-Attribute-Value Model and Terminological Models into a Common Ontology to Enable Data Integration and Decision Support
Authors: Bouaud Jacques  , Guézennec Gilles  , Séroussi Brigitte
Published: February 2025

The integration of clinical information models and termino-ontological models into a unique ontological framework is highly desirable for it facilitates data integration and management using the same formal mechanisms for both data concepts and information model components. This is particularly true for knowledge-based decision support tools that aim to take advantage of all facets of semantic web technologies in merging ontological reasoning, concept classification, and rule-based inferences. We present an ontology template that combines generic data model components with (parts of) existing termino-ontological resources. The approach is developed for the guideline-based decision support module on breast cancer management within the DESIREE European project. The approach is based on the entity attribute value model and could be extended to other domains.
Topic Repetition in Conversations on Different Days as a Sign of Dementia
Authors: Shinkawa Kaoru  , Yamada Yasunori
Published: February 2025

Detecting early signs of dementia in everyday situations becomes more and more important in a rapidly aging society. Language dysfunctions are recognized as the prominent signs of dementia. Previous computational studies characterized these language dysfunctions by using acoustic and linguistic features for detecting dementia. However, they mainly investigated language dysfunctions collected from patients during neuropsychological tests. Language dysfunctions observed during regular conversations in everyday situations received little attention. One of the dysfunctions associated with dementia which is frequently observed in regular conversations is the repetition of a topic on different days. In this study, we propose a feature to characterize topic repetition in conversations on different days. We used conversational data obtained from a daily monitoring service of eight elderly people, two of whom had dementia. Through the analysis of topic extraction with latent Dirichlet allocation, we found that the frequency of topic repetition was significantly higher in people with dementia than in the control group. The results suggest that our proposed feature for identifying topic repetition in regular conversations on different days might be used for detecting dementia.
Approach Towards an Evidence-Oriented Knowledge and Data Acquisition for the Optimization of Interdisciplinary Care in Dentistry and General Medicine
Authors: Seitz Max W.  , Haux Christian  , Knaup Petra  , Schubert Ingrid  , Listl Stefan
Published: February 2025

Associations between dental and chronic-systemic diseases were observed frequently in medical research, however the findings of this research have so far found little relevance in everyday clinical treatment. Major problems are the assessment of evidence for correlations between such diseases and how to integrate current medical knowledge into the intersectoral care of dentists and general practitioners. On the example of dental and chronic-systemic diseases, the Dent@Prevent project develops an interdisciplinary decision support system (DSS), which provides the specialists with information relevant for the treatment of such cases. To provide the physicians with relevant medical knowledge, a mixed-methods approach is developed to acquire the knowledge in an evidence-oriented way. This procedure includes a literature review, routine data analyses, focus groups of dentists and general practitioners as well as the identification and integration of applicable guidelines and Patient Reported Measures (PRMs) into the treatment process. The developed mixed methods approach for an evidence-oriented knowledge acquisition indicates to be applicable and supportable for interdisciplinary projects. It can raise the systematic quality of the knowledge-acquisition process and can be applicable for an evidence-based system development. Further research is necessary to assess the impact on patient care and to evaluate possible applicability in other interdisciplinary areas.
A Modular Ontology for Modeling Service Provision in a Communication Network for Coordination of Care
Authors: Cardoso Sonia  , Aimé Xavier  , Meininger Vincent  , Grabli David  , Melo Mora Luis Felipe  , Cohen K. Bretonnel  , Charlet Jean
Published: February 2025

This paper presents a modular ontology of health care in the context in Amyotrophic Lateral Sclerosis. 4 modules cover socio-environmental, medical, and care coordination aspects of the domain. They are organized by a core module. Its goal is to understand interruptions in health care provision in the context of a neurodegenerative disease.
A System for Supporting Development and Update of the International Classification of Health Interventions (ICHI)
Authors: Donada Marc  , Della Mea Vincenzo  , Cumerlato Megan  , Rankin Nicole  , Madden Richard
Published: February 2025

The International Classification of Health Interventions (ICHI) is a member of the WHO Family of International Classifications, being developed to provide a common tool for reporting and analysing health interventions for statistical purposes. A web-based platform for classification development and update has been specifically developed to support the initial development step and then, after final approval, the continuous revision and update of the classification. The platform provides features for classification editing, versioning, comment management and URI identifiers. During the last 12 months it has been used for developing the ICHI Beta version, replacing the previous process based on the exchange of Excel files. At November 2017, 90 users have provided input to the development of the classification, which has resulted in 2913 comments and 2971 changes in the classification, since June 2017. Further work includes the development of an URI API for machine to machine communication, following the model established for ICD-11.
A Personalized Risk Stratification Platform for Population Lifetime Healthcare
Authors: Daowd Ali  , Abidi Samina Raza  , Abusharekh Ashraf  , Abidi Syed Sibte Raza
Published: February 2025

Chronic diseases are the leading cause of death worldwide. It is well understood that if modifiable risk factors are targeted, most chronic diseases can be prevented. Lifetime health is an emerging health paradigm that aims to assist individuals to achieve desired health targets, and avoid harmful lifecycle choices to mitigate the risk of chronic diseases. Early risk identification is central to lifetime health. In this paper, we present a digital health-based platform (PRISM) that leverages artificial intelligence, data visualization and mobile health technologies to empower citizens to self-assess, self-monitor and self-manage their overall risk of major chronic diseases and pursue personalized chronic disease prevention programs. PRISM offers risk assessment tools for 5 chronic conditions, 2 psychiatric disorders and 8 different cancers.
Extent of Use of Electronic Records in Children's Primary Care and Public Health in Europe
Authors: Rigby Michael  , Kühne Grit  , Greenfield Rama  , Majeed Azeem  , Blair Mitch E.
Published: February 2025

EHRs are widely seen as a key resource in modern health care. For children not only is primary care vital, but also case-based public health systems can help ensure that all children receive immunisation and other preventive programmes. A European study showed that in 2016 in the 30 EU and EEA countries, 19 countries had widespread use of EHRs in children's primary care, while 20 countries had case-based child public health systems. However, the results show a bias of disadvantage for poorer or smaller countries. More study is needed in this area.
Automated Error Detection in Physiotherapy Training
Authors: Jovanović Marko  , Seiffarth Johannes  , Kutafina Ekaterina  , Jonas Stephan M.
Published: February 2025

Background: Manual skills teaching, such as physiotherapy education, requires immediate teacher feedback for the students during the learning process, which to date can only be performed by expert trainers.
How to Measure Physical Motion and the Impact of Individualized Feedback in the Field of Rehabilitation of Geriatric Trauma Patients
Authors: Altenbuchner Amelie  , Haug Sonja  , Kretschmer Rainer  , Weber Karsten
Published: February 2025

Background: This preparatory study accelerates an implementation of individualized monitoring and feedback of physical motion using conventional motion trackers in the rehabilitation process of geriatric trauma patients. Regaining mobility is accompanied with improved quality of life in persons of very advanced age recovering from fragility fractures.
Development and Evaluation of Cognitive Games to Promote Health and Wellbeing in Elderly People with Mild Cognitive Impairment
Authors: Scase Mark  , Kreiner Karl  , Ascolese Antonio
Published: February 2025

Background: In Europe the number of elderly people is increasing. This population growth has resulted in higher healthcare costs. The purpose of this project was to try to promote active ageing in people aged 65–80 with mild cognitive impairment through cognitive games delivered via a tablet computer.
Importance of Personalized Health-Care Models: A Case Study in Activity Recognition
Authors: Zdravevski Eftim  , Lameski Petre  , Trajkovik Vladimir  , Pombo Nuno  , Garcia Nuno
Published: February 2025

Novel information and communication technologies create possibilities to change the future of health care. Ambient Assisted Living (AAL) is seen as a promising supplement of the current care models. The main goal of AAL solutions is to apply ambient intelligence technologies to enable elderly people to continue to live in their preferred environments. Applying trained models from health data is challenging because the personalized environments could differ significantly than the ones which provided training data. This paper investigates the effects on activity recognition accuracy using single accelerometer of personalized models compared to models built on general population. In addition, we propose a collaborative filtering based approach which provides balance between fully personalized models and generic models. The results show that the accuracy could be improved to 95% with fully personalized models, and up to 91.6% with collaborative filtering based models, which is significantly better than common models that exhibit accuracy of 85.1%. The collaborative filtering approach seems to provide highly personalized models with substantial accuracy, while overcoming the cold start problem that is common for fully personalized models.
Towards Building an Attained Index of Passenger Ship Fire Safety
Authors: Koromila Ioanna A.  , Spyrou Kostas J.  , Themelis Nikos  , Ioannou Sofia
Published: February 2025

Protection against fire is one of the pillars of maritime safety. In particular for passenger ships, experience has shown that a fire onboard can incur catastrophic consequences. Although the maritime fire safety legislation requires compliance with prescriptive regulations of fire prevention, protection and extinction, the concept of “safety equivalence” has enabled substantial innovations in the design of some passenger ships. The assessment framework could be strengthened by introducing a probabilistic formulation. The present study is an attempt for a step forward, aiming to develop the elements and the structure of a probabilistic attained fire safety index. The probability of fire ignition, the reliability of the installed suppression systems and the expected loss due to fire growth, are key elements contributing to the novel assessment which is sensitive to design detail, such as the space layout and the interior design materials used. The distribution of fatalities has been selected as the most appropriate “loss” function. As an application, two adjacent fire zones accommodating passenger spaces will be examined. The loss function distribution is derived from a batch of fire and evacuation simulations. The method is extendable to address the entire ship.
Experimental Investigation of the Hydrodynamic Performance of the ISWEC 1:20 Scaled Device
Authors: Sirigu Sergej Antonello  , Bonfanti Mauro  , Passione Biagio  , Begovic Ermina  , Bertorello Carlo  , Dafnakis Panagiotis  , Bracco Giovanni  , Giorcelli Ermanno  , Mattiazzo Giuliana
Published: February 2025

An experimental campaign test has performed on the 1:20 scaled moored model of ISWEC (Inertial Sea Wave Energy Converter). The annual productivity of the device is strictly correlated to the hydrodynamic properties of the floater. Therefore, is fundamental to carry out an experimental investigation of the hydrodynamic performance of the floater and compare the results with the numerical codes. In this paper the experimental campaign is described. Two different mooring configurations were tested to understand the different influence of mooring forces on the floater dynamics. The non-dimensional Response Amplitude Operator (RAO) is representative of the hydrodynamic performances of the floater. Therefore, the device with both the mooring configurations has been tested in regular waves of constant wave steepness 1/50. Conclusions are commenting the differences a between numerical and experimental results and the impact of nonlinearities on hydrodynamic performances.