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Comparison of Electronic Prescription Systems in the European Union: Benchmarking Development, Use, and Future Trends

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IEEE Journal of Biomedical and Health informatics 2025年第5期29卷 3712-3722页

作者： Bruthans, Jan Duftschmid, Georg Hammar, Tora Kardas, Przemyslaw Bertalan, Lorant Hug, Martin J. Bullow, Cille Muscat, Hugo Agius Lillevali, Anett Jormanainen, Vesa Fernandez-Llimos, Fernando Fidalgo, Luz Karanikas, Haralampos Nyssen, Marc Popescu, Nirvana Cudejkova, Maria Tachkov, Konstantin Hadziabdic, Maja Ortner Neofytou, Marios Dauksiene, Jurgita Huisman, Anneke Ryan, Brid Kasparans, Aris Napoleoni, Matteo Perfili, Mirko Smet, Daisy Calafiore, Matthieu Stanimirovic, Dalibor Czech Technical University in Prague Department of Biomedical Technology Faculty of Biomedical Engineering Prague Czech Republic Medical University of Vienna Section for Medical Information Management Center for Medical Data Science Vienna Austria Linnaeus University eHealth Institute Kalmar Sweden Medical University of Lodz Medication Adherence Research Centre Department of Family Medicine Lodz Poland Semmelweis University Faculty of Health Sciences Budapest Hungary Medical Center-University of Freiburg Pharmacy Freiburg Germany Bispebjerg and Frederiksberg University Hospital Department of Clinical Pharmacology Copenhagen Denmark Ministry for Health and Active Ageing Directorate for Health Information & Research Valletta Malta Estonian Health Insurance Fund Tallinn Estonia University of Helsinki Department of Public Health Faculty of Medicine Helsinki Finland University of Porto Applied Molecular Biosciences Unit Laboratory of Pharmacology Faculty of Pharmacy Porto Portugal Ministry of Health Madrid Spain University of Thessaly Department of Computer Science and Biomedical Informatics Lamia Greece Vrije Universiteit Brussel Department of Public Health Brussel Belgium National University of Science and Technology POLITEHNICA Bucharest Computer Science Department Bucharest Romania National Health Information Centre Bratislava Slovakia Medical University of Sofia Faculty of Pharmacy Sofia Bulgaria University of Zagreb Faculty of Pharmacy and Biochemistry Zagreb Croatia University of Cyprus Electronic Health Laboratory Computer Science Department Nicosia Cyprus Lithuanian University of Health Sciences Drug technology and Social Pharmacy Department Vilnius Lithuania Royal Dutch Pharmacists Association The Hague Netherlands Health Service Executive ePharmacy Programme Dublin Ireland National Health Service of Latvia Riga Latvia GE Healthcare Milan Italy Bambino Gesu' Children's Research Hospital IRCCS Rome Italy Agence eSanté Luxembourg Luxe

While Electronic Prescription Systems (EPS) adoption varies across EU Member States, there's a lack of comprehensive comparative analysis. Existing studies focus on single EPSs, employ diverse methodologies, and lack up-to-date data. This study fills this gap by providing a comprehensive overview of EPS development, functionalities, and usage statistics in each EU Member State. Most EU Member States widely adopted EPS by 2022, with exceptions including Germany, France, and Luxembourg, where pilot projects or just plans existed at that time. Out of the 27 EPSs, 25 employ a similar design featuring a central server and end-user software or web-based applications. Among these, 22 are structured as single national systems. The fundamental technical solution is remarkably similar across the EU. Despite these similarities, functionalities, authentication methods, prescription validity, and medication coverage differ significantly among EPSs. A multinational team, including co-authors from each EU Member State, collected data using a structured questionnaire. The study underscores the need for standardized methodologies in EPS research and emphasizes the importance of comprehensive comparative analysis to inform healthcare policies and digitalization efforts. © 2013 IEEE.

关键词： Electronic health record

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A Bio-Inspired Metaheuristics Algorithm for Reduction of Coverage Holes in Optimizing the Performance of Next Generation Wireless Network (NGWN) 2nd

A Bio-Inspired Metaheuristics Algorithm for Reduction of Cov...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Ajibade, Samuel-Soma M. Abdul Majeed, Anwar P. P. Oyebode, Oluwadare Joshua Jasser, Muhammed Basheer Ajayi, Babatunde Adedotun Alebiosu, David Olayemi Luo, Yang Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan47500 Malaysia Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan Petaling Jaya47500 Malaysia Civil and Environmental Engineering Afe Babalola University Ekiti Ado Ekiti Nigeria Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan Petaling Jaya47500 Malaysia Department of Cyber Security Faculty of Computing and Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

While Next Generation Wireless Networks are widely used, controlling them can be challenging regardless of whether a network is fully connected, there may be coverage gaps. In this study, we provide a technique for placing nodes in a wireless network while making sure that there is a minimum coverage hole in the network coverage area. We use the firefly algorithm to close the coverage gap in order to increase connectivity. The Firefly algorithm provides a novel solution for overcoming the drawbacks of traditional overburden hole detection techniques, such as downtime, low accuracy, and high operating costs, by installing sensor nodes in the appropriate locations and scheduling those network devices. The algorithm looks for the best locations where the NGWN sensor may be placed inside the designated zone in order to reduce the coverage hole area. The algorithm will treat randomly generated solutions as fireflies, and it will determine how bright they are based on how well they perform in terms of the goal function. Simulation results show that the proposed method decreases the covering hole area by 96%. The provided approach was suitable for NGWN since it reduced computation time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Operating costs

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A categorization of performance measures for estimated non-linear associations between an outcome and continuous predictors

arXiv

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arXiv 2025年

作者： Ullmann, Theresa Heinze, Georg Abrahamowicz, Michal Perperoglou, Aris Sauerbrei, Willi Schmid, Matthias Dunkler, Daniela Institute of Clinical Biometrics Center for Medical Data Science Medical University of Vienna Vienna Austria Department of Epidemiology & Biostatistics McGill University Montreal Canada Statistics and Data Science Innovation Hub GSK Stevenage United Kingdom Institute of Medical Biometry and Statistics Faculty of Medicine and Medical Center University of Freiburg Freiburg Germany Department of Medical Biometry Informatics and Epidemiology University Hospital Bonn Bonn Germany

In regression analysis, associations between continuous predictors and the outcome are often assumed to be linear. However, modeling the associations as non-linear can improve model fit. Many flexible modeling techniques, like (fractional) polynomials and spline-based approaches, are available. Such methods can be systematically compared in simulation studies, which require suitable performance measures to evaluate the accuracy of the estimated curves against the true data-generating functions. Although various measures have been proposed in the literature, no systematic overview exists so far. To fill this gap, we introduce a categorization of performance measures for evaluating estimated non-linear associations between an outcome and continuous predictors. This categorization includes many commonly used measures. The measures can not only be used in simulation studies, but also in application studies to compare different estimates to each other. We further illustrate and compare the behavior of different performance measures through some examples and a Shiny app. © 2025, CC BY.

关键词： Prediction models

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Echo-E3Net: Efficient Endo-Epi Spatio-Temporal Network for Ejection Fraction Estimation

arXiv

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arXiv 2025年

作者： Heidari, Moein Bozorgpour, Afshin Zarif-Fakharnia, AmirHossein Merhof, Dorit Hacihaliloglu, Ilker School of Biomedical Engineering University of British Columbia VancouverBC Canada Faculty of Informatics and Data Science University of Regensburg Regensburg Germany School of Electrical Engineering Iran University of Science and Technology Tehran Iran Department of Radiology University of British Columbia VancouverBC Canada Department of Medicine University of British Columbia VancouverBC Canada

Left ventricular ejection fraction (LVEF) is a critical metric for assessing cardiac function, widely used in diagnosing heart failure and guiding clinical decisions. Despite its importance, conventional LVEF estimation remains time-consuming and operator-dependent. Recent deep learning advancements have enhanced automation, yet many existing models are computationally demanding, hindering their feasibility for real-time clinical applications. Additionally, the interplay between spatial and temporal features is crucial for accurate estimation but is often overlooked. In this work, we propose Echo-E3Net, an efficient Endo-Epi spatio-temporal network tailored for LVEF estimation. Our method introduces the Endo-Epi Cardial Border Detector (E2CBD) module, which enhances feature extraction by leveraging spatial and temporal landmark cues. Complementing this, the Endo-Epi Feature Aggregator (E2FA) distills statistical descriptors from backbone feature maps, refining the final EF prediction. These modules, along with a multi-component loss function tailored to align with the clinical definition of EF, collectively enhance spatial-temporal representation learning, ensuring robust and efficient EF estimation. We evaluate Echo-E3Net on the EchoNet-Dynamic dataset, achieving a RMSE of 5.15 and an R2 score of 0.82, setting a new benchmark in efficiency with 6.8 million parameters and only 8.49G Flops. Our model operates without pre-training, data augmentation, or ensemble methods, making it well-suited for real-time point-of-care ultrasound (PoCUS) applications. Our Code is publicly available on GitHub. © 2025, CC BY-NC-SA.

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Addressing Missing Modality Challenges in MRI Images: A Comprehensive Review

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Computational Visual Media 2025年第2期11卷 241-268页

作者： Reza Azad Mohammad Dehghanmanshadi Nika Khosravi Julien Cohen-Adad Dorit Merhof Faculty of Electrical Engineering and Information Technology RWTH Aachen University Aachen Germany School of Advanced Technologies in Medicine Iran University of Medical Sciences Tehran Iran NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal Canada Functional Neuroimaging Unit CRIUGM University of Montreal Montreal Canada MILA Quebec AI Institute Montreal Canada Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Magnetic resonance imaging (MRI) is one of the most prevalent imaging modalities used for diagnosis, treatment planning, and outcome control in various medical conditions. MRI sequences provide physicians with the ability to view and monitor tissues at multiple contrasts within a single scan and serve as input for automated systems to perform downstream tasks. However, in clinical practice, there is usually no concise set of identically acquired sequences for a whole group of patients. As a consequence, medical professionals and automated systems both face difficulties due to the lack of complementary information from such missing sequences. This problem is well known in computer vision, particularly in medical image processing tasks such as tumor segmentation, tissue classification, and image generation. With the aim of helping researchers, this literature review examines a significant number of recent approaches that attempt to mitigate these problems. Basic techniques such as early synthesis methods, as well as later approaches that deploy deep learning, such as common latent space models, knowledge distillation networks, mutual information maximization, and generative adversarial networks (GANs) are examined in detail. We investigate the novelty, strengths, and weaknesses of the aforementioned strategies. Moreover, using a case study on the segmentation task, our survey offers quantitative benchmarks to further analyze the effectiveness of these methods for addressing the missing modalities challenge. Furthermore, a discussion offers possible future research directions.

关键词： Surveys Knowledge engineering Deep learning Image segmentation Visualization Magnetic resonance imaging Benchmark testing Mutual information Faces Tumors

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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

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IEEE Transactions on Mobile Computing 2025年

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science Faculty of Environment Science and Economy ExeterEX4 4QF United Kingdom Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China University of Oslo Department of Informatics Oslo0316 Norway

Mobile Edge Computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. © 2025 IEEE.

关键词： Convex optimization

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Bayesian curriculum generation in sparse reward reinforcement learning environments

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Engineering science and Technology, an International Journal 2025年 66卷

作者： Akgün, Onur Üre, N. Kemal Istanbul Technical University Graduate School Mechatronics Engineering Program Maslak Istanbul 34469 Turkey Turkish-German University Faculty of Engineering Mechatronics Engineering Department Beykoz Istanbul 34820 Turkey Istanbul Technical University Faculty of Computer and Informatics Engineering Artificial Intelligence and Data Engineering Department Maslak Istanbul 34469 Turkey Istanbul Technical University Artificial Intelligence and Data Science Research Center Maslak Istanbul 34469 Turkey

This paper introduces the Bayesian Curriculum Generation Algorithm, a sophisticated approach for curriculum learning in sparse reward reinforcement learning contexts. Diverging from traditional methodologies, this algorithm utilizes Bayesian networks to dynamically create tasks by altering problem parameters, thereby impacting task difficulty. It operates independently from the core reinforcement learning algorithm, enabling compatibility with a variety of RL techniques. A notable feature of our algorithm is its capability for unsupervised task classification, utilizing a clustering process applicable to both image outputs and scalar values. This method efficiently categorizes tasks based on difficulty, circumventing the need for exhaustive training for each task. However, the effectiveness of this approach is contingent upon the presence of definable parameters within the environment and necessitates domain expertise to determine the appropriate tool, be it image output or scalar parameter analysis. The algorithm selects tasks from a curated pool corresponding to specific difficulty levels and adapts according to the agent's performance. Successful task completion triggers the generation of more complex tasks, whereas encountering challenges results in the maintenance or minor adjustment of task complexity. This adaptive feature significantly enhances the efficiency of the learning process. Empirical evaluations conducted in various environments, characterized by maze-like structures, discrete or continuous settings, and the presence of adversarial entities hindering the agent's mission, demonstrate the algorithm's efficacy and its superiority over conventional methods. The Bayesian Curriculum Generation Algorithm represents a significant advancement in reinforcement learning, providing a dynamic and adaptable solution for complex learning challenges. © 2025 The Authors

关键词： Bayesian networks Curriculum learning Reinforcement learning Unsupervised clustering

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BFGTP: A BERT-Guided Two-Stage Molecular Representation Learning Framework for Toxicity Prediction

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IEEE Journal of Biomedical and Health informatics 2025年 PP卷 PP页

作者： Hu, Kaimiao He, Yuan Wei, Jianguo Sun, Changming Geng, Jie Wei, Leyi Su, Ran Tianjin University College of Intelligence and Computing Tianjin30000 China CSIRO Data61 SydneyNSW1710 Australia Tianjin University Tianjin Key Laboratory of Cardiovascular Emergency and Critical Care Tianjin Municipal Science and Technology Bureau Department of Cardiology Chest Hospital Tianjin China Macao Polytechnic University Centre for Artificial Intelligence driven Drug Discovery Faculty of Applied Science China Xiamen University School of Informatics Xiamen China

Accurate prediction of molecular toxicity is vital for drug development. Most mainstream methods rely on fingerprints or graph-based feature extraction, the emergence of large language models (LLMs) offers new prospects for molecular representation learning in toxicity prediction. Although several studies attempt to leverage LLMs to integrate molecular sequence data for pretraining molecular representations, certain limitations remain. Current LLM-based approaches usually utilize solely on class embedding features, overlooking the rich information in sequence embedding. Moreover, integrating pre-trained molecular representations with multi-modal molecular data may further enhance performance in toxicity prediction. To address these challenges, we propose BFGTP, a BERT-guided two-stage molecular representation learning framework for toxicity prediction. Firstly, we design independent encoders for molecular descriptions of three modalities, where the fingerprint encoder with dual level attention mechanisms effectively integrates multi-category fingerprints. Then, the two-stage guide strategy is introduced to fully utilize the prior knowledge of LLMs, employing contrastive learning to align and fuse the tri-modal representations and knowledge distillation to align predicted value distributions. BFGTP ultimately combines fingerprint and graph representations to predict molecular toxicity. Experiments on seven toxicity datasets show that BFGTP outperforms baselines, achieving the highest AUC on five datasets and the best average performance across five evaluation metrics. Ablation studies, t-SNE visualization and case study confirm the effectiveness of BFGTP's components and its ability to capture meaningful molecular representations. ©2013 IEEE.

关键词： Contrastive Learning

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Opportunities and Challenges Surrounding the Use of Wearable Sensor Bracelets for Infectious Disease Detection During Hajj: Qualitative Interview Study

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JMIR Formative Research 2025年 9卷 e60484页

作者： Maddah, Noha Verma, Arpana Ainsworth, John Division of Informatics Imaging and Data Sciences School of Health Sciences Faculty of Biology Medicine and Health Manchester Academic Health Science Centre Centre for Health Informatics The University of Manchester Manchester United Kingdom Department of Health Services and Hospitals Administration Faculty of Economics and Administration King Abdulaziz University Jeddah Saudi Arabia Division of Population Health Health Services Research & Primary Care Faculty of Biology Medicine and Health Manchester Academic Health Sciences Centre The University of Manchester Manchester United Kingdom

Background: Wearable sensor bracelets have gained interest for their ability to detect symptomatic and presymptomatic infections through alterations in physiological indicators. Nevertheless, the use of these devices for public health surveillance among attendees of large-scale events such as hajj, the Islamic religious mass gathering held in Saudi Arabia, is currently in a nascent phase. Objective: This study aimed to explore hajj stakeholders’ perspectives on the use of wearable sensor bracelets for disease detection. Methods: We conducted a qualitative, theoretically informed, interview-based study from March 2022 to October 2023 involving a diverse sample of hajj stakeholders, including technology experts, health care providers, and hajj service providers. The study was guided by the task-technology fit model and the unified theory of acceptance and use of technology to provide a comprehensive understanding of the factors influencing the acceptance and use of the technology. Semistructured in-depth interviews were used to capture perspectives on using wearable sensor bracelets for infectious disease detection during hajj. Thematic analysis of interview transcripts was conducted. Results: A total of 14 individuals were interviewed. In total, 4 main themes and 13 subthemes emerged from the study, highlighting crucial challenges, considerations, recommendations, and opportunities in the use of wearable sensor bracelets for the presymptomatic detection of infectious diseases during hajj. Implementing wearable sensor bracelets for disease detection during hajj faces obstacles from multiple perspectives, encompassing users, implementing stakeholders, and technological factors. Hajj stakeholders were concerned about the substantial financial and operational barriers. The motivation of implementing stakeholders and users is essential for the acceptance and uptake of devices during hajj. Successful integration of wearables into the hajj surveillance system depends on sev

关键词： artificial intelligence hajj infectious diseases presymptomatic detection task-technology fit unified theory of acceptance and use of technology wearable sensor

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Non-invasive brain stimulation reorganises effective connectivity during a working memory task in individuals with Neurofibromatosis Type 1

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NeuroImage: Reports 2025年第2期5卷 100258-100258页

作者： Litwińczuk, Marta Czime Garg, Shruti Williams, Stephen R. Green, Jonathan Lea-Carnall, Caroline Trujillo-Barreto, Nelson J. School of Health Sciences University of Manchester Manchester United Kingdom Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester United Kingdom Geoffrey Jefferson Brain Research Centre Manchester Academic Health Science Centre Manchester United Kingdom School of Psychology Manchester Metropolitan University Manchester United Kingdom

Introduction: In a previous study, we examined the effect of atDCS on working memory task performance and modulation of the inhibitory neurotransmitter, gamma-aminobutyric acid (GABA), in the dorsolateral prefrontal cortex (dlPFC). The present study investigates whether tDCS modulates effective connectivity during the task, specifically assessing whether tDCS alters interactions between neuronal populations. Methods: Eighteen adolescents with Neurofibromatosis Type 1 (NF1) completed a single-blind sham-controlled cross-over randomised tDCS trial (with the anode at F3 and cathode at Cz). Dynamic causal modelling was used to estimate the effective connectivity between regions that showed working memory effects from the fMRI. Group-level inferences for between sessions (pre- and post-stimulation) and stimulation type (tDCS and sham) effects were carried out using the parametric empirical Bayes approach. A correlation analysis was performed to relate the estimated effective connectivity parameters of left dlPFC pre-tDCS and post-tDCS to the concentration of GABA measured via magnetic resonance spectroscopy (MRS-GABA). Next, correlation analysis was repeated using all working memory performance and all pre-tDCS and post-tDCS connectivity parameters. Results: It was found that tDCS decreased average excitatory connectivity from dlPFC to left superior frontal gyrus and increased average excitatory connectivity to left globus pallidus. Further, reduced average intrinsic (inhibitory) connectivity of left dlPFC was associated with lower MRS-GABA. However, none of the connectivity parameters of dlPFC showed any association with performance on a working memory task. Conclusions: These findings suggest that tDCS reorganised connectivity from frontal to fronto-striatal connectivity. As tDCS-related changes were not specific to the effect of working memory, they may have impacted general cognitive control processes. In addition, by reducing MRS-GABA, tDCS might make dlPFC more sen

关键词： Effective connectivity GABA Magnetic resonance spectroscopy Neurofibromatosis Non-invasive brain stimulation tDCS Working memory

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