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Getting Ready for the European Health Data Space (Ehds): The Iderha's Plan to Align with the Ehds Requirements for the Secondary Use of Health Data

SSRN

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SSRN 2024年

作者： Hussein, Rada Balaur, Irina Burmann, Anja Ćwiek-Kupczyńska, Hanna Gadiya, Yojana Ghosh, Soumyabrata Jayathissa, Prabath Katsch, Florian Kremer, Andreas Lähteenmäki, Jaakko Meng, Zhaoling Morasek, Kathrin Rancourt, Rebecca C. Satagopam, Venkata Sauermann, Stefan Scheider, Simon Stamm, Tanja A. Muehlendyck, Christian Gribbon, Philip Ludwig Boltzmann Institute for Digital Health and Prevention Salzburg Austria Luxembourg Centre for Systems Biology University of Luxembourg Luxembourg Fraunhofer Institute for Software and Systems Engineering Dortmund Germany Discovery Research Screening Port Hamburg Germany Theodor Stern Kai 7 Frankfurt60590 Germany Bonn-Aachen International Center for Information Technology University of Bonn Bonn Germany Center for Medical Data Science Medical University of Vienna Vienna Austria ITTM S.A. Luxembourg VTT Technical Research Centre of Finland Ltd Espoo Finland Clinical Modeling and Evidence Integration Sanofi CambridgeMA United States Institute of Outcomes Research Center for Medical Data Science Medical University of Vienna Austria Ludwig Boltzmann Institute for Arthritis and Rehabilitation Vienna Austria Medical School Berlin Berlin Germany Faculty Life Science Engineering FH Technikum Wien Vienna Austria TU Dortmund Dortmund Germany Johnson & Johnson Medical GmbH Norderstedt Germany

Objective: The European Health Data Space (EHDS) shapes the digital transformation of healthcare in Europe. The EHDS regulations will also accelerate the use of health data for research, innovation, policy-making, and regulatory activities for secondary use of data (known as EHDS2). The Integration of heterogeneous Data and Evidence towards Regulatory and HTA Acceptance (IDERHA) project builds one of the first pan-European health data spaces in alignment with the EHDS2 requirements, addressing lung cancer as a *** and Methods: In this study, we conducted a comprehensive review of the EHDS regulations and technical requirements for EHDS2. Mainly utilizing the results of the Joint Action Towards the European Health Data Space (TEHDAS) project to identify the framework of IDERHA’s alignment with EHDS. We also conducted an internal webinar and an external workshop with EHDS experts to share expertise on the EHDS requirements and ***: We identified the minimum-set of requirements for aligning IDERHA infrastructure with the planned EHDS regulations, including user journey, concepts, terminologies, and standards. The future IDERHA framework (i.e., platform architecture, standardization approaches, documentation, etc.) will be developed ***: The IDERHA's alignment plan with EHDS2 necessitates the implementation of three categories of standardization for: data discoverability (DCAT-AP), enabling semantics interoperability (OMOP), and health data exchange (DICOM and FHIR). The main challenge is that some standards are still being refined, e.g., the extension of the DCAT-AP (HealthDCAT-AP). Additionally, extensions to the OHDSI OMOP CMD for representing the Patient-Generated Health Data are still needed. Finally, proper mapping between standards (FHIR/OMOP) is a prerequisite for proper data ***: Collaboration, sharing lessons learned, and creating synergy among the current EHDS initiatives and projects are critical

关键词： Electronic data interchange

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Detecting potentially harmful and protective suicide-related content on twitter: A machine learning approach

arXiv

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arXiv 2021年

作者： Metzler, Hannah Baginski, Hubert Niederkrotenthaler, Thomas Garcia, David Section for Science of Complex Systems Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Austria Unit Suicide Research & Mental Health Promotion Department of Social and Preventive Medicine Center for Public Health Medical University of Vienna Austria Complexity Science Hub Vienna Austria Institute of Interactive Systems and Data Science Department of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Institute of Globally Distributed Open Research and Education Austria Institute of Information Systems Engineering Vienna University of Technology Vienna Austria

Background Research has repeatedly shown that exposure to suicide-related news media content is associated with suicide rates, with some content characteristics likely having harmful and others potentially protective effects. Although good evidence exists for a few selected characteristics, systematic and large-scale investigations are missing. Moreover, the growing importance of social media, particularly among young adults, calls for studies on the effects of content posted on these platforms. Objective This study applies natural language processing and machine learning methods to classify large quantities of social media data according to characteristics identified as potentially harmful or beneficial in media effects research on suicide and prevention. Methods We manually labeled 3202 English tweets using a novel annotation scheme that classifies suicide-related tweets into 12 categories. Based on these categories, we trained a benchmark of machine learning models for a multi-class and a binary classification task. As models, we included a majority classifier, an approach based on word frequency (TF-IDF with a linear SVM) and two state-of-the-art deep learning models (BERT, XLNet). The first task classified postings into six main content categories, which are particularly relevant for suicide prevention based on previous evidence. These included personal stories of either (1) suicidal ideation and attempts or (2) coping and recovery, calls for action intending to spread either (3) problem awareness or (4) prevention-related information, (5) reporting of suicide cases, and (6) other tweets irrelevant to these five categories. The second classification task was binary, and separated postings in the 11 categories that refer to actual suicide, from postings in the off-topic category, which use suicide-related terms in another meaning or context. Results In both tasks, the performance of the two deep learning models was very similar and better than the majority or th

关键词： Learning algorithms

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Personality Estimation using Demographic Data in a Personality-based Recommender System: A Proposal 2019

Personality Estimation using Demographic Data in a Personali...

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Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services

作者： Iman Paryudi Ahmad Ashari A. Min Tjoa Department of Informatics Pancasila University Jakarta Indonesia Department of Computer Science and Electronics Gadjah Mada University Yogyakarta Indonesia Institute of Software Technology and Interactive Systems Vienna University of Technology Vienna Austria

ISBN: (纸本)9781450371797

Collaborative filtering in a recommender system has a weakness called cold start problem. One way to resolve this problem is by using personality traits that can be automatically predicted from the status that the users write in social media like Facebook and Twitter. The problem with this method is that a user of such system must have at least one account in at least one social media and must write at least one status with certain length. We propose to use the combination of personality traits and demographic data to overcome this problem. Previous studies reveal that personality traits are influenced by age and gender. By using these findings, we will build models to predict personality traits from such demographic data. The modeling will be conducted by means of classification and association rule methods. Novel domains will be used in the proposed system, namely sports and hobbies.

关键词： Five Factor Model k-Nearest Neighbor Classification Method Naïve Bayes Recommender System for Hobbies Association Rule Demographic Data Decision Tree Recommender System for Sports Personality Traits Big Five

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Social media emotion macroscopes reflect emotional experiences in society at large

arXiv

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arXiv 2021年

作者： Garcia, David Pellert, Max Lasser, Jana Metzler, Hannah Graz University of Technology Institute of Interactive Systems and Data Science Department of Computer Science and Biomedical Engineering Graz8010 Austria Complexity Science Hub Vienna Vienna1080 Austria Medical University of Vienna Center for Medical Statistics Informatics and Intelligent Systems Vienna1090 Austria Medical University of Vienna Department of Social and Preventive Medicine Center for Public Health Vienna1090 Austria

Social media generate data on human behaviour at large scales and over long periods of time, posing a complementary approach to traditional methods in the social sciences1. Millions of texts from social media can be processed with computational methods to study emotions2,3 over time and across regions4,5. However, recent research has shown weak correlations between social media emotions and affect questionnaires at the individual level6,7 and between static regional aggregates of social media emotion and subjective well-being at the population level8, questioning the validity of social media data to study emotions. Yet, to date, no research has tested the validity of social media emotion macroscopes to track the temporal evolution of emotions at the level of a whole society. Here we present a pre-registered prediction study that shows how gender-rescaled time series of Twitter emotional expression at the national level substantially correlate with aggregates of self-reported emotions in a weekly representative survey in the United Kingdom. A follow-up exploratory analysis shows a high prevalence of third-person references in emotionally-charged tweets, indicating that social media data provide a way of social sensing the emotions of others9 rather than just the emotional experiences of users. These results show that, despite the issues that social media have in terms of representativeness10 and algorithmic confounding11, the combination of advanced text analysis methods with user demographic information in social media emotion macroscopes can provide measures that are informative of the general population beyond social media users. Copyright © 2021, The Authors. All rights reserved.

关键词： Social networking (online)

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AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions

arXiv

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arXiv 2022年

作者： Iftikhar, Sundas Gill, Sukhpal Singh Song, Chenghao Xu, Minxian Aslanpour, Mohammad Sadegh Toosi, Adel N. Du, Junhui Wu, Huaming Ghosh, Shreya Chowdhury, Deepraj Golec, Muhammed Kumar, Mohit Abdelmoniem, Ahmed M. Cuadrado, Felix Varghese, Blesson Rana, Omer Dustdar, Schahram Uhlig, Steve School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom University of Kotli Azad Jammu & Kashmir Azad Kashmir Kotli Pakistan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Software Systems and Cybersecurity Faculty of Information Technology Monash University Australia Csiro DATA61 Australia Center for Applied Mathematics Tianjin University Tianjin China The Pennsylvania State University PA United States Naya Raipur India Abdullah Gül University Kayseri Turkey Department of Information Technology National Institute of Technology Jalandhar India Spain School of Computer Science University of St Andrews United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria

Resource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature of workload, and the unpredictability of fog/edge computing environments have made resource management even more challenging to be considered in the fog landscape. Recently Artificial Intelligence (AI) and Machine Learning (ML) based solutions are adopted to solve this problem. AI/ML methods with the capability to make sequential decisions like reinforcement learning seem most promising for these type of problems. But these algorithms come with their own challenges such as high variance, explainability, and online training. The continuously changing fog/edge environment dynamics require solutions that learn online, adopting changing computing environment. In this paper, we used standard review methodology to conduct this Systematic Literature Review (SLR) to analyze the role of AI/ML algorithms and the challenges in the applicability of these algorithms for resource management in fog/edge computing environments. Further, various machine learning, deep learning and reinforcement learning techniques for edge AI management have been discussed. Furthermore, we have presented the background and current status of AI/ML-based Fog/Edge Computing. Moreover, a taxonomy of AI/ML-based resource management techniques for fog/edge computing has been proposed and compared the existing techniques based on the proposed taxonomy. Finally, open challenges and promising future research directions have been identified and discussed in the area of AI/ML-based fog/edge computing. Copyright © 2022, The Authors. All rights reserved.

关键词： Edge computing

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Finite Volume Methods for Transient Modeling of Gas Pipelines

Finite Volume Methods for Transient Modeling of Gas Pipeline...

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IEEE International Energy Conference (ENERGYCON)

作者： Malley, Conor O. Kourounis, Drosos Hug, Gabriela Schenk, Olaf Vienna University of Technology Institute of Software Technology and Interactive Systems Vienna Austria

ISBN: (纸本)9781538636695

Given the increasing importance of multi-energy carrier system modeling, this paper focuses on modeling the dynamics that occur in natural gas transmission lines. In high-pressure pipelines, these dynamics are governed by a set of hyperbolic partial differential equations. Dissipative finite volume discretization schemes are proposed that honor the discrete maximum principle. The convergence of the proposed discretization schemes is investigated and compared with existing methods suggested in the literature for natural gas transmission such as the implicit cell-centered method. High order time discretization methods are also tested and their suitability with respect to the maximum principle is discussed.

关键词： transient gas flow finite volume multi-energy carrier systems

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ARCH-COMP21 Category Report: Stochastic Models 8th

ARCH-COMP21 Category Report: Stochastic Models

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8th International Workshop on Applied Verification of Continuous and Hybrid systems, ARCH 2021

作者： Abate, Alessandro Blom, Henk Bouissou, Marc Cauchi, Nathalie Chraibi, Hassane Delicaris, Joanna Haesaert, Sofie Hartmanns, Arnd Khaled, Mahmoud Lavaei, Abolfazl Ma, Hao Mallik, Kaushik Niehage, Mathis Remke, Anne Schupp, Stefan Shmarov, Fedor Soudjani, Sadegh Thorpe, Adam Turcuman, Vlad Zuliani, Paolo University of Oxford Oxford United Kingdom Delft University of Technology Delft Netherlands France University of Münster Germany TU Eindhoven Eindhoven Netherlands University of Twente Enschede Netherlands Technical University of Munich Germany ETH Zurich Switzerland Max Planck Institute for Software Systems Germany TU Wien Vienna Austria University of Manchester Manchester United Kingdom Newcastle University Newcastle upon Tyne United Kingdom University of New Mexico United States

This report presents the results of a friendly competition for formal verification and policy synthesis of stochastic models. It also introduces new benchmarks within this category, and recommends next steps for this category towards next year’s edition of the competition. The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid systems (ARCH) in Spring/Summer 2021. © 2021, EasyChair. All rights reserved.

关键词： Stochastic systems

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Expert sourcing to support the identification of model elements in system descriptions 10th

Expert sourcing to support the identification of model eleme...

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10th International Conference on software Quality Days, SWQD 2018

作者： Sabou, Marta Winkler, Dietmar Petrovic, Sanja Institute of Software Technology and Interactive Systems Vienna University of Technology Vienna Austria

ISBN: (纸本)9783319714394

Context. Expert sourcing is a novel approach to support quality assurance: it relies on methods and tooling from crowdsourcing research to split model quality assurance tasks and parallelize task execution across several expert users. Typical quality assurance tasks focus on checking an inspection object, e.g., a model, towards a reference document, e.g., a requirements specification, that is considered to be correct. For example, given a text-based system description and a corresponding model such as an Extended Entity Relationship (EER) diagram, experts are guided towards inspecting the model based on so-called expected model elements (EMEs). EMEs are entities, attributes and relations that appear in text and are reflected by the corresponding model. In common inspection tasks, EMEs are not explicitly expressed but implicitly available via textual descriptions. Thus, a main improvement is to make EMEs explicit by using crowdsourcing mechanisms to drive model quality assurance among experts. Objective and Method. In this paper, we investigate the effectiveness of identifying the EMEs through expert sourcing. To that end, we perform a feasibility study in which we compare EMEs identified through expert sourcing with EMEs provided by a task owner who has a deep knowledge of the entire system specification text. Conclusions. Results of the data analysis show that the effectiveness of the crowdsourcing-style EME acquisition is influenced by the complexity of these EMEs: entity EMEs can be harvested with high recall and precision, but the lexical and semantic variations of attribute EMEs hamper their automatic aggregation and reaching consensus (these EMEs are harvested with high precisions but limited recall). Based on these lessons learned we propose a new task design for expert sourcing EMEs. © Springer International Publishing AG 2018.

关键词： Quality assurance

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Fragile, yet resilient: Adaptive decline in a collaboration network of firms

arXiv

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arXiv 2020年

作者： Schweitzer, Frank Casiraghi, Giona Tomasello, Mario V. Garcia, David ETH Zurich Switzerland Complexity Science Hub Vienna Austria Institute of Interactive Systems and Data Science Graz University of Technology Austria

The dynamics of collaboration networks of firms follow a life-cycle of growth and decline. That does not imply they also become less resilient. Instead, declining collaboration networks may still have the ability to mitigate shocks from firms leaving, and to recover from these losses by adapting to new partners. To demonstrate this, we analyze 21.500 R&D collaborations of 14.500 firms in six different industrial sectors over 25 years. We calculate time-dependent probabilities of firms leaving the network and simulate drop-out cascades, to determine the expected dynamics of decline. We then show that deviations from these expectations result from the adaptivity of the network, which mitigates the decline. These deviations can be used as a measure of network resilience. Copyright © 2020, The Authors. All rights reserved.

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Validating daily social media macroscopes of emotions

arXiv

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arXiv 2021年

作者： Pellert, Max Metzler, Hannah Matzenberger, Michael Garcia, David Sony Computer Science Laboratories 6 Rue Amyot Paris75005 France Institute of Interactive Systems and Data Science Graz University of Technology Inffeldgasse 16C Graz8010 Austria Complexity Science Hub Vienna Josefstädter Straße 39 Vienna1080 Austria Section for Science of Complex Systems Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Spitalgasse 23 Vienna1090 Austria Institute of Globally Distributed Open Research and Education Sweden Der Standard Vordere Zollamtsstraße 13 Vienna1030 Austria

Measuring sentiment in social media text has become an important practice in studying emotions at the macroscopic level. However, this approach can suffer from methodological issues like sampling biases and measurement errors. To date, it has not been validated if social media sentiment can actually measure the temporal dynamics of mood and emotions aggregated at the level of communities. We ran a large-scale survey at an online newspaper to gather daily mood self-reports from its users, and compare these with aggregated results of sentiment analysis of user discussions. We find strong correlations between text analysis results and levels of self-reported feelings, as well as between inter-day changes of both measurements. We replicate these results using sentiment data from Twitter. We show that a combination of supervised text analysis methods based on novel deep learning architectures and unsupervised dictionary-based methods have high agreement with the time series of aggregated mood measured with self-reports. Our findings indicate that macro level dynamics of feelings expressed on an online platform can be tracked with social media text, especially in situations of high mood variability. © 2021, CC BY.

关键词： Sentiment analysis

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