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arXiv 2022年

作者： Schüller, Peter Costeira, João Paulo Crowley, James Grosinger, Jasmin Ingrand, Félix Köckemann, Uwe Saffotti, Alessandro Welss, Martin Knowledge-based Systems Group Technische Universität Wien Vienna Austria Institute for Systems and Robotics Técnico Lisboa Lisbon Portugal Univ. Grenoble Alpes CNRS Inria Grenoble France Center for Applied Autonomous Sensor Systems Örebro University Sweden LAAS-CNRS University of Toulouse France Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS Germany

Progress in several areas of computer science has been enabled by comfortable and efficient means of experimentation, clear interfaces, and interchangable components, for example using OpenCV for computer vision or ROS for robotics. We describe an extension of the Acumos system towards enabling the above features for general AI applications. Originally, Acumos was created for telecommunication purposes, mainly for creating linear pipelines of machine learning components. Our extensions include support for more generic components with gRPC/Protobuf interfaces, automatic orchestration of graphically assembled solutions including control loops, sub-component topologies, and event-based communication, and provisions for assembling solutions which contain user interfaces and shared storage areas. We provide examples of deployable solutions and their interfaces. The framework is deployed at http://***/ and its source code is managed as an open source Eclipse project. © 2022, CC BY.

关键词： User interfaces

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A Randomized Neural Network for Data Streams

A Randomized Neural Network for Data Streams

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International Joint Conference on Neural Networks

作者： Mahardhika Pratama Plamen P. Angelov Jie Lu Edwin Lughofer Manjeevan Seera C. P. Lim School of Engineering and Mathematical Sciences La Trobe University School of Computing and Communication Lancaster University Center for Quantum Computation and Intelligent Systems University of Technology Sydney Department of knowledge-based mathematical system Johannes Kepler University Faculty of Engineering Computing and Science Swinburne University of Technology (Sarawak Campus) Institute of Intelligent System Research and Innovation Deakin University

ISBN: (纸本)9781509061839

Randomized neural network (RNN) is a highly feasible solution in the era of big data because it offers a simple and fast working principle in processing dynamic and evolving data streams. This paper proposes a novel RNN, namely recurrent type-2 random vector functional link network (RT2McRVFLN), which provides a highly scalable solution for data streams in a strictly online and integrated framework. It is built upon the psychologically inspired concept of metacognitive learning, which covers three basic components of human learning: what-to-learn, how-to-learn, and when-to-learn. The what-to-learn selects important samples on the fly with the use of online active learning scenario, which renders our algorithm an online semi-supervised algorithm. The how-to-learn process combines an open structure of evolving concept and a randomized learning algorithm of random vector functional link network (RVFLN). The efficacy of the RT2McRVFLN has been numerically validated through two real-world case studies and comparisons with its counterparts, which arrive at a conclusive finding that our algorithm delivers a tradeoff between accuracy and simplicity.

关键词： Evolving Fuzzy systems Fuzzy Neural Networks Type-2 Fuzzy systems Sequential Learning

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Emerging Practices for Mapping and Linking Life Sciences Data Using RDF - A Case Series

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SSRN 2018年

作者： Marshall, M. Scott Boyce, Richard Deus, Helena F. Zhao, Jun Willighagen, Egon L. Samwald, Matthias Pichler, Elgar Hajagos, Janos Prud’hommeaux, Eric Stephens, Susie Informatics Institute University of Amsterdam Amsterdam Netherlands Dept. of Medical Statistics and Bioinformatics Leiden University Medical Center Leiden Netherlands Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States Digital Enterprise Research Institute National University of Ireland at Galway Ireland Department of Zoology University of Oxford Oxford United Kingdom Division of Molecular Toxicology Institute of Environmental Medicine Karolinska Institutet Stockholm Sweden Section for Medical Expert and Knowledge-Based Systems Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Vienna Austria Vienna University of Technology Vienna Austria W3C HCLSIG W3C CambridgeMA United States Stony Brook University School of Medicine Stony BrookNY United States World Wide Web Consortium MIT Cambridge United States Johnson & Johnson

Members of the W3C Health Care and Life Sciences Interest Group (HCLS IG) have published a variety of genomic and drug-related datasets as Resource Description Framework (RDF) triples. This experience has helped the interest group define a general data workflow for mapping health care and life science (HCLS) data to RDF and linking it with other Linked Data sources. This paper presents the workflow along with four case studies that demonstrate both the workflow and many of the challenges that may be faced when using the workflow to create new Linked Data resources. The first case study describes the creation of linked RDF data from microarray datasets while the second discusses a linked RDF dataset created from a knowledge base of drug therapies and drug targets. The third case study describes the creation of an RDF index of biomedical concepts present in unstructured clinical reports and how this index was linked to a drug side-effect knowledge base. The final case study describes the initial development of a linked dataset from a knowledge base of small molecules. This paper also provides a detailed set of recommended practices for creating and publishing Linked Data sources in the HCLS domain in such a way that they are discoverable and useable by users, Semantic Web agents, and applications. These practices are based on the cumulative experience of the Linked Open Drug Data (LODD) task force of the HCLS IG. While no single set of recommendations can address all of the heterogeneous information needs that exist within the HCLS domains, practitioners wishing to create Linked Data should find the recommendations useful for identifying the tools, techniques, and practices employed by earlier developers. In addition to clarifying available methods for producing Linked Data, the recommendations for metadata should also make the discovery and consumption of Linked Data easier. © 2018, The Authors. All rights reserved.

关键词： Mapping

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Exploring nutritional supplement use for countering respiratory tract infections through an X (formerly Twitter)-based survey

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Current Research in Biotechnology 2025年 9卷

作者： Singla, Rajeev K. Mondal, Himel Singla, Shailja De, Ronita Behzad, Sahar Găman, Mihnea-Alexandru Sai Chandragiri, Siva Cenanovic, Merisa Patra, Jayanta Kumar Depew, Jennifer R. Boyina, Hemanth Kumar Maigoro, Abdulkadir Yusif Lee, Soojin Atrooz, Omar M. Das, Gitishree Schultz, Fabien Abdallah, Emad Mohamed Chopra, Hitesh Ahmad, Jamil Gautam, Rupesh K. Patnaik, Sourav S. Goh, Bey Hing Babiaka, Smith B. Vats, Sharad Okoh, Michael P. Ahmed, Atallah F. Dubey, Ankit Kumar Lordan, Ronan Subramani, Parasuraman Aiya Singh, Amit Kumar Alvarez-Suarez, José M. Chellappan, Dinesh Kumar Paswan, Shravan Kumar Semwal, Prabhakar Khan, Johra Sheshe, Sadeeq Sethiya, Neeraj Kumar Karpiński, Tomasz M. Riaz, Muhammad Emam-Djomeh, Zahra Gupta, Girish Kumar Madaan, Reecha Kumar, Suresh Choudhary, Neeraj Parisi, Salvatore Willschke, Harald Pirgozliev, Vasil Rayan, Rehab A. Ritschl, Valentin Mondal, Shaikat Zengin, Gokhan Verma, Pritt Kapoor, Bhupinder Gulati, Monica Balla, Gareeballah Osman Adam Le, Dan Khoa Pittalà, Valeria El-Demerdash, Amr Khalid, Garba M. Simal-Gandara, Jesus Alzahrani, Qushmua E. Russo, Gian Luigi Kharat, Kiran R. Bishayee, Anupam Wang, Dongdong Orhan, Ilkay Erdogan Ullah, Hammad Heinrich, Michael Baral, Bikash Tzvetkov, Nikolay T. Yeung, Andy Wai Kan Dias-Ferreira, João M.L. Olea, Scarlett Perez Mohanta, Yugal Kishore Kureshi, Azazahemad A. Supuran, Claudiu T. Rani, Neeraj Gundamaraju, Rohit Mulholland, Eoghan Joseph Lonardo, Sara Di Dinkova-Kostova, Albena T. González-Burgos, Elena Hritcu, Lucian Badhe, Pravin Singh, Abhilasha Al-Rimawi, Fuad Sureda, Antoni Abiri, Rambod Braidy, Nady Kapral, Lorenz Abdullahi, A.N. Medina, Christhian Delfino Villanueva Sheridan, Helen Lucarini, Massimo Durazzo, Alessandra Giampieri, Francesca Barreca, Davide Maria, Witkowska Anna Andrade, José Carlos Fimognari, Carmela Akram, Faizan Tikhonov, Aleksei Battino, Maurizio Oladipupo, Akolade R. Emerald, Mila Efferth, Thomas Amrani, Said Echeverría, Javier Maria Louka, Anna Tripathi, Surya Kant Fiebich, Bernd L. Es-Safi, Nour Eddine Khan Department of Critical Care Medicine and Institutes for Systems Genetics Frontiers Science Center for Disease-related Molecular Network West China Hospital Sichuan University Chengdu China Department of Physiology All India Institute of Medical Sciences Jharkhand Deoghar India iGlobal Research and Publishing Foundation New Delhi India Evidence-based Phytotherapy and Complementary Medicine Research Center Alborz University of Medical Sciences Karaj Iran Department of Hematology Center for Clinical and Basic Research (CCBR Clinic) Bucharest Romania Department of Cellular and Molecular Pathology Stefan S. Nicolau Institute of Virology Romanian Academy Bucharest Romania Department of Pathology University of Oklahoma Health Sciences Center Oklahoma City OK United States Sarajevo Bosnia and Herzegovina Research Institute of Integrative Life Sciences Dongguk University-Seoul Goyang-Si South Korea United States University Institute of Pharma Sciences Chandigarh University Punjab Mohali 140413 India Department of Microbiology and Molecular Biology College of Bioscience and Biotechnology Chungnam National University Daejeon 34134 South Korea Department of Biological Sciences Mutah University Jordan Ethnopharmacology & Zoopharmacognosy Research Group Department of Agriculture and Food Sciences Neubrandenburg University of Applied Sciences Germany Department of Biology College of Science Qassim University Buraydah 51452 Saudi Arabia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai 602105 India Department of Human Nutrition The University of Agriculture Peshawar Khyber Pakhtunkhwa 25120 Pakistan Department of Pharmacology Indore Institute of Pharmacy IIST Campus Rau (M.P.) Indore 453331 India The University of Texas Southwestern Medical Center Dallas TX United States Sunway Biofunctional Molecules Discovery Centre (SBMDC) School of Medical and Life Sciences Sunway University S

Background: Respiratory tract infections are a common health issue, driving interest in preventive strategies like nutritional supplements, while evidence on their usage and effectiveness remains limited. In this context, social media platforms, particularly X (formerly Twitter), provide a unique opportunity to gather large-scale public health-related data. Objectives: In this study, we aimed to survey participants’ uses and opinions on nutritional supplements in prevention or treatment of respiratory tract infections, by using X. Methods: A survey was conducted between 1st and 15th December 2022. A single open-ended question “Which are the best dietary supplements to counteract respiratory infections?“ was asked. One week after the start of the survey, a poll was posted to get more relevant information and boost the survey's reach. Total endorsements were calculated for each tweet posted as the total sum of replies, retweets, and likes. Results: The open-ended question received a total of 118 retweets, 39 quotes, and 371 likes, while the poll received 56 retweets, 13 quotes, and 67 likes. A total of 495 replies, 2,251 retweets, 5,118 likes, and 148 quotes were received for the question and its related tweets. Vitamin D (1,607 endorsements), zinc (1,347 endorsements), vitamin C (803 endorsements), magnesium (694 endorsements), and honey (661 endorsements) were the nutritional supplements that received most endorsements. Conclusion: Various foods, drinks, and natural ingredients have been suggested as potentially helpful for counteracting respiratory infections. Approximately half of respondents indicated using such supplements for themselves. The result of this study supports the idea that the X platform can be used as an effective survey tool to study global health-related behaviours and trends. © 2025 The Authors

关键词： Bacteria COVID-19 Nutritional supplements Phytomedicine Respiratory tract infections X (formerly Twitter)

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An update on genomic CDS, a complex ontology for pharmacogenomics and clinical decision support 3

An update on genomic CDS, a complex ontology for pharmacogen...

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3rd International Workshop on OWL Reasoner Evaluation, ORE 2014 - Co-located with the Vienna Summer of Logic, VSL 2014

作者： Minarro-Giménez, José Antonio Samwald, Matthias Section for Medical Expert and Knowledge-Based Systems Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Vienna Austria

Genetic data can be used to optimize drug treatment based on the ge-netic profiles of individual patients, thereby reducing adverse drug events and improving the efficacy of pharmacotherapy. The Genomic Clinical Decision Support (Genomic CDS) ontology utilizes Web Ontology Language 2 (OWL 2) reasoning for this task. The ontology serves a clear-cut medical use case that requires challenging OWL 2 DL reasoning. We present an update of the Ge-nomic CDS ontology which covers a significantly larger number of clinical de-cision support rules and where inconsistencies presented in previous versions of the ontology have been removed.

关键词： Ontology

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Plea for use of intelligent information and communication technologies in infection surveillance and benchmarking by healthcare institutions

Plea for use of intelligent information and communication te...

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7th International Conference on Health Informatics, HEALTHINF 2014 - Part of 7th International Joint Conference on Biomedical Engineering systems and Technologies, BIOSTEC 2014

作者： Koller, Walter Adlassnig, Klaus-Peter Rappelsberger, Andrea Blacky, Alexander Clinical Institute of Hospital Hygiene Medical University of Vienna Vienna General Hospital Waehringer Strasse 18-20 1090 Vienna Austria Section for Medical Expert and Knowledge-Based Systems Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Spitalgasse 23 1090 Vienna Austria Medexter Healthcare GmbH Borschkegasse 7/5 1090 Vienna Austria

ISBN: (纸本)9789897580109

Top healthcare and medicine depends on the implementation of best practice methods, which include surveillance of and benchmarking with defined quality indicators. Using healthcare-associated infection (HAI) surveillance as an example, we put forward arguments in favour of automated intelligent information and communication technologies. Assessment studies with our fully automated detection and monitoring system for HAIs not only revealed much higher precision of surveillance results and much less time investment compared with conventional surveillance, but also a potential emerged for amendments and adaptations regarding new input categories or new surveillance outputs desired by clinicians, administrators, and health authorities. In this way, intelligent information and communication technologies are becoming indispensable in building affordable "safety nets" for quality assurance and benchmarking, based on fully automated and intelligent data and knowledge management. These in turn form the backbone of high-level healthcare, patient safety, and error prevention. Copyright © 2014 SCITEPRESS - Science and Technology Publications. All rights reserved.

关键词： knowledge management

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Acceptability and difficulties of (fuzzy) decision support systems in clinical practice

Acceptability and difficulties of (fuzzy) decision support s...

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Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： Christian J. Schuh Jeroen S. de Bruin Walter Seeling Center for Medical Statistics Informatics and Intelligent Systems (CeMSIIS) Medical Expert and Knowledge-Based Systems Medical University of Vienna Vienna Austria

ISBN: (纸本)9781479903467

Medical knowledge in modern health care is vast and constantly changing, as well as expanding. The envisioned role of computer programs in health care is perhaps the most important. The provisioning of clinical decision support systems (CDSS) would enable the discovery of patterns in health data which might be important for the fight against incorrect diagnosis. Medicine uses empirical knowledge about superficial associations between symptoms and diseases, and uncertainty is without any doubt a central, critical fact about medical reasoning. Many of intelligent CDSS are based on the fuzzy set theory, which describes medical complex systems mathematical model in terms of linguistic rules. Considering the fuzzy nature of the data in a medical environment, it becomes obvious that the ability of managing uncertainty turns to be a crucial issue for clinical fuzzy decision support systems (CFDSS). Since the potential of medical decision making was first realized, hundreds of articles introducing decision support systems (DSS) have been published in the last three decades. But even today, only few systems are in clinical use, and their full potential for optimizing the healthcare system is far from realized. Clinician's acceptance and utilization of CDSS depends on its workflow-oriented context sensitive accessibility and availability at the point of care, and on the integration into a hospital information system (HIS). This paper describes advantages and disadvantages of several approaches, and experiences, gained by clinical use of two introduced CFDSS, integrated in the HIS of the Vienna General Hospital, to analyze the little use of CDSS in today's clinical practice.

关键词： Decision support systems Medical diagnostic imaging knowledge based systems Hospitals Insulin Surveillance

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Semantically enabling pharmacogenomic data for the realization of personalized medicine (vol 13, pg 201, 2012)

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PHARMACOGENOMICS 2012年第5期13卷 628-628页

作者： Samwald, M. Coulet, A. Huerga, I Section for Medical Expert & Knowledge-Based Systems Center for Medical Statistics Informatics & Intelligent Systems Medical University of Vienna Spitalgasse 23 A-1090 Vienna Austria Institute of Software Technology & Interactive Systems University of Technology Vienna Favoritenstrasse 9–11/188 A-1040 Vienna Austria LORIA – INRIA Nancy–Grand-Est Campus Scientifique-BP 239 54506 Vandoeuvre-lès-Nancy Cedex France Elsevier 1600 John F Kennedy Blvd. Suite 1800 Philadelphia PA 19103-2899 USA Predictive Medicine Inc. 37 Mansion Drive Topsfield MA 01983 USA Tetherless World Constellation Rensselaer Polytechnic Institute 110 8th Street Winslow 2143 Troy NY 12180 USA Division of Biomedical Statistics & Informatics Department of Health Sciences Research Mayo Clinic 200 First Street SW Rochester MN 55901 USA Genomics Education Initiative 609 Sycamore Avenue Fullerton CA 92831 USA Department of Chemistry & Chemical Biology Northeastern University 360 Huntington Avenue Boston MA 02115 USA World Wide Web Consortium/MIT 32 Vassar Street Cambridge MA 02140 USA Department of Biology School of Computer Science Institute of Biochemistry Carleton University 1125 Colonel By Drive Ottawa ON K1T 2T4 Canada Department of Medical Statistics & Bioinformatics Leiden University Medical Center/Informatics Institute University of Amsterdam Einthovenweg 20 2333 ZC Leiden The Netherlands

Understanding how each individual’s genetics and physiology influences pharmaceutical response is crucial to the realization of personalized medicine and the discovery and validation of pharmacogenomic biomarkers is key to its success. However, integration of genotype and phenotype knowledge in medical information systems remains a critical challenge. The inability to easily and accurately integrate the results of biomolecular studies with patients’ medical records and clinical reports prevents us from realizing the full potential of pharmacogenomic knowledge for both drug development and clinical practice. Herein, we describe approaches using Semantic Web technologies, in which pharmacogenomic knowledge relevant to drug development and medical decision support is represented in such a way that it can be efficiently accessed both by software and human experts. We suggest that this approach increases the utility of data, and that such computational technologies will become an essential part of personalized medicine, alongside diagnostics and pharmaceutical products.

关键词： clinical decision support systems knowledge representation ontologies personalized medicine pharmacogenomics translational medicine

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Decision support systems in a medical environment and its limitations

Decision support systems in a medical environment and its li...

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2010 International Conference on Artificial Intelligence, ICAI 2010

作者： Schuh, Ch.J. Center for Medical Statistics Informatics and Intelligent Systems Medical Expert and Knowledge-Based Systems Medical University of Vienna Spitalgasse 23 A-1090 Vienna Austria

ISBN: (纸本)9781601321480

The envisioned role of computer programs in health care is perhaps the most important. Everything we know today in medicine might not have been possible without the valuable contribution of computers. Uncertainty is the central, critical fact about medical reasoning. Particularly in an intensive care unit (ICU), where decisions must often be made quickly, or that physicians will follow it rather than openly or surreptitiously limiting care on their own. The provisioning of Decision Support systems (DSS) in an ICU would enable the discovery of patterns in health data which might be important for the fight against incorrect diagnosis. Many of this intelligent DSS are based on the fuzzy set theory which describes complex systems mathematical model in terms of linguistic rules. Fuzzy set theory and fuzzy logic, founded in 1965 by Lotfi A. Zadeh, are a highly suitable and applicable basis for developing such systems in medicine. Over the past three decades experience has shown that the expectations were not always fulfilled in this field. This paper addresses the capabilities as well as limitations of DSS on three concrete applications. All systems established as real-time applications at an ICU and have reached the state of extensive clinical integration and testing at the Vienna general hospital.

关键词： Decision making

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Size invariant shape recognition by modulated competitive Neural Circuit

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International Journal of Information and Communication Technology 2007年第1期1卷 76-88页

作者： Wu, Lai Si Jain, Lakhmi Knowledge-Based Intelligent Engineering Systems Center University of South Australia Adelaide Mawson Lakes SA 5095 Australia

This paper addresses the problem of size invariant shape recognition based on scale transformation within a modulated competition neural layer. In this paper we discuss the advantages of the application of a neural network to pattern recognition and study how the traditional automatic target recognition can fail to recognise a known pattern due to either a size change or background clutter and distortion. The model construction is based on neurophysiology experiments on vision systems. The Neural Circuit Simulation studies undertaken demonstrate the effectiveness of the proposed model in recognising 2D objects in non-ideal visual conditions. Copyright © 2007 Inderscience Enterprises Ltd.

关键词： Automatic target recognition

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