89 research outputs found
Improving the Quality and Utility of Electronic Health Record Data through Ontologies
The translational research community, in general, and the Clinical and Translational Science Awards (CTSA) community, in particular, share the vision of repurposing EHRs for research that will improve the quality of clinical practice. Many members of these communities are also aware that electronic health records (EHRs) suffer limitations of data becoming poorly structured, biased, and unusable out of original context. This creates obstacles to the continuity of care, utility, quality improvement, and translational research. Analogous limitations to sharing objective data in other areas of the natural sciences have been successfully overcome by developing and using common ontologies. This White Paper presents the authorsâ rationale for the use of ontologies with computable semantics for the improvement of clinical data quality and EHR usability formulated for researchers with a stake in clinical and translational science and who are advocates for the use of information technology in medicine but at the same time are concerned by current major shortfalls. This White Paper outlines pitfalls, opportunities, and solutions and recommends increased investment in research and development of ontologies with computable semantics for a new generation of EHRs
KG-Hub-building and exchanging biological knowledge graphs.
MOTIVATION: Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking.
RESULTS: Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract-transform-load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification.
AVAILABILITY AND IMPLEMENTATION: https://kghub.org
Managing healthcare transformation towards P5 medicine (Published in Frontiers in Medicine)
Health and social care systems around the world are facing radical organizational, methodological and technological paradigm changes to meet the requirements for improving quality and safety of care as well as efficiency and efficacy of care processes. In this theyâre trying to manage the challenges of ongoing demographic changes towards aging, multi-diseased societies, development of human resources, a health and social services consumerism, medical and biomedical progress, and exploding costs for health-related R&D as well as health services delivery. Furthermore, they intend to achieve sustainability of global health systems by transforming them towards intelligent, adaptive and proactive systems focusing on health and wellness with optimized quality and safety outcomes.
The outcome is a transformed health and wellness ecosystem combining the approaches of translational medicine, 5P medicine (personalized, preventive, predictive, participative precision medicine) and digital health towards ubiquitous personalized health services realized independent of time and location. It considers individual health status, conditions, genetic and genomic dispositions in personal social, occupational, environmental and behavioural context, thus turning health and social care from reactive to proactive. This requires the advancement communication and cooperation among the business actors from different domains (disciplines) with different methodologies, terminologies/ontologies, education, skills and experiences from data level (data sharing) to concept/knowledge level (knowledge sharing). The challenge here is the understanding and the formal as well as consistent representation of the world of sciences and practices, i.e. of multidisciplinary and dynamic systems in variable context, for enabling mapping between the different disciplines, methodologies, perspectives, intentions, languages, etc. Based on a framework for dynamically, use-case-specifically and context aware representing multi-domain ecosystems including their development process, systems, models and artefacts can be consistently represented, harmonized and integrated. The response to that problem is the formal representation of health and social care ecosystems through an system-oriented, architecture-centric, ontology-based and policy-driven model and framework, addressing all domains and development process views contributing to the system and context in question.
Accordingly, this Research Topic would like to address this change towards 5P medicine. Specifically, areas of interest include, but are not limited:
⢠A multidisciplinary approach to the transformation of health and social systems
⢠Success factors for sustainable P5 ecosystems
⢠AI and robotics in transformed health ecosystems
⢠Transformed health ecosystems challenges for security, privacy and trust
⢠Modelling digital health systems
⢠Ethical challenges of personalized digital health
⢠Knowledge representation and management of transformed health ecosystems
Table of Contents:
04 Editorial: Managing healthcare transformation towards P5
medicine
Bernd Blobel and Dipak Kalra
06 Transformation of Health and Social Care SystemsâAn
Interdisciplinary Approach Toward a Foundational
Architecture
Bernd Blobel, Frank Oemig, Pekka Ruotsalainen and Diego M. Lopez
26 Transformed Health EcosystemsâChallenges for Security,
Privacy, and Trust
Pekka Ruotsalainen and Bernd Blobel
36 Success Factors for Scaling Up the Adoption of Digital
Therapeutics Towards the Realization of P5 Medicine
Alexandra Prodan, Lucas Deimel, Johannes Ahlqvist, Strahil Birov,
Rainer Thiel, Meeri Toivanen, Zoi Kolitsi and Dipak Kalra
49 EU-Funded Telemedicine Projects â Assessment of, and
Lessons Learned From, in the Light of the SARS-CoV-2
Pandemic
Laura Paleari, Virginia Malini, Gabriella Paoli, Stefano Scillieri,
Claudia Bighin, Bernd Blobel and Mauro Giacomini
60 A Review of Artificial Intelligence and Robotics in
Transformed Health Ecosystems
Kerstin Denecke and Claude R. Baudoin
73 Modeling digital health systems to foster interoperability
Frank Oemig and Bernd Blobel
89 Challenges and solutions for transforming health ecosystems
in low- and middle-income countries through artificial
intelligence
Diego M. LĂłpez, Carolina Rico-Olarte, Bernd Blobel and Carol Hullin
111 Linguistic and ontological challenges of multiple domains
contributing to transformed health ecosystems
Markus Kreuzthaler, Mathias Brochhausen, Cilia Zayas, Bernd Blobel
and Stefan Schulz
126 The ethical challenges of personalized digital health
Els Maeckelberghe, Kinga Zdunek, Sara Marceglia, Bobbie Farsides
and Michael Rigb
PICT-DPA: A Quality-Compliance Data Processing Architecture to Improve the Performance of Integrated Emergency Care Clinical Decision Support System
Emergency Care System (ECS) is a critical component of health care systems by providing acute resuscitation and life-saving care. As a time-sensitive care operation system, any delay and mistake in the decision-making of these EC functions can create additional risks of adverse events and clinical incidents. The Emergency Care Clinical Decision Support System (EC-CDSS) has proven to improve the quality of the aforementioned EC functions. However, the literature is scarce on how to implement and evaluate the EC-CDSS with regard to the improvement of PHOs, which is the ultimate goal of ECS. The reasons are twofold: 1) lack of clear connections between the implementation of EC-CDSS and PHOs because of unknown quality attributes; and 2) lack of clear identification of stakeholders and their decision processes. Both lead to the lack of a data processing architecture for an integrated EC-CDSS that can fulfill all quality attributes while satisfying all stakeholdersâ information needs with the goal of improving PHOs. This dissertation identified quality attributes (PICT: Performance of the decision support, Interoperability, Cost, and Timeliness) and stakeholders through a systematic literature review and designed a new data processing architecture of EC-CDSS, called PICT-DPA, through design science research. The PICT-DPA was evaluated by a prototype of integrated PICT-DPA EC-CDSS, called PICTEDS, and a semi-structured user interview. The evaluation results demonstrated that the PICT-DPA is able to improve the quality attributes of EC-CDSS while satisfying stakeholdersâ information needs. This dissertation made theoretical contributions to the identification of quality attributes (with related metrics) and stakeholders of EC-CDSS and the PICT Quality Attribute model that explains how EC-CDSSs may improve PHOs through the relationships between each quality attribute and PHOs. This dissertation also made practical contributions on how quality attributes with metrics and variable stakeholders could be able to guide the design, implementation, and evaluation of any EC-CDSS and how the data processing architecture is general enough to guide the design of other decision support systems with requirements of the similar quality attributes
Development of linguistic linked open data resources for collaborative data-intensive research in the language sciences
Making diverse data in linguistics and the language sciences open, distributed, and accessible: perspectives from language/language acquistiion researchers and technical LOD (linked open data) researchers. This volume examines the challenges inherent in making diverse data in linguistics and the language sciences open, distributed, integrated, and accessible, thus fostering wide data sharing and collaboration. It is unique in integrating the perspectives of language researchers and technical LOD (linked open data) researchers. Reporting on both active research needs in the field of language acquisition and technical advances in the development of data interoperability, the book demonstrates the advantages of an international infrastructure for scholarship in the field of language sciences. With contributions by researchers who produce complex data content and scholars involved in both the technology and the conceptual foundations of LLOD (linguistics linked open data), the book focuses on the area of language acquisition because it involves complex and diverse data sets, cross-linguistic analyses, and urgent collaborative research. The contributors discuss a variety of research methods, resources, and infrastructures. Contributors Isabelle Barrière, Nan Bernstein Ratner, Steven Bird, Maria Blume, Ted Caldwell, Christian Chiarcos, Cristina Dye, Suzanne Flynn, Claire Foley, Nancy Ide, Carissa Kang, D. Terence Langendoen, Barbara Lust, Brian MacWhinney, Jonathan Masci, Steven Moran, Antonio Pareja-Lora, Jim Reidy, Oya Y. Rieger, Gary F. Simons, Thorsten Trippel, Kara Warburton, Sue Ellen Wright, Claus Zin
CLARIN
The book provides a comprehensive overview of the Common Language Resources and Technology Infrastructure â CLARIN â for the humanities. It covers a broad range of CLARIN language resources and services, its underlying technological infrastructure, the achievements of national consortia, and challenges that CLARIN will tackle in the future. The book is published 10 years after establishing CLARIN as an Europ. Research Infrastructure Consortium
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Examining university student satisfaction and barriers to taking online remote exams
Recent years have seen a surge in the popularity of online exams at universities, due to the greater convenience and flexibility they offer both students and institutions. Driven by the dearth of empirical data on distance learning students' satisfaction levels and the difficulties they face when taking online exams, a survey with 562 students at The Open University (UK) was conducted to gain insights into their experiences with this type of exam. Satisfaction was reported with the environment and exams, while work commitments and technical difficulties presented the greatest barriers. Gender, race and disability were also associated with different levels of satisfaction and barriers. This study adds to the increasing number of studies into online exams, demonstrating how this type of exam can still have a substantial effect on students experienced in online learning systems and
technologies
User-centered semantic dataset retrieval
Finding relevant research data is an increasingly important but time-consuming task in daily research practice. Several studies report on difficulties in dataset search, e.g., scholars retrieve only partial pertinent data, and important information can not be displayed in the user interface. Overcoming these problems has motivated a number of research efforts in computer science, such as text mining and semantic search. In particular, the emergence of the Semantic Web opens a variety of novel research perspectives. Motivated by these challenges, the overall aim of this work is to analyze the current obstacles in dataset search and to propose and develop a novel semantic dataset search. The studied domain is biodiversity research, a domain that explores the diversity of life, habitats and ecosystems. This thesis has three main contributions: (1) We evaluate the current situation in dataset search in a user study, and we compare a semantic search with a classical keyword search to explore the suitability of semantic web technologies for dataset search. (2) We generate a question corpus and develop an information model to figure out on what scientific topics scholars in biodiversity research are interested in. Moreover, we also analyze the gap between current metadata and scholarly search interests, and we explore whether metadata and user interests match. (3) We propose and develop an improved dataset search based on three components: (A) a text mining pipeline, enriching metadata and queries with semantic categories and URIs, (B) a retrieval component with a semantic index over categories and URIs and (C) a user interface that enables a search within categories and a search including further hierarchical relations. Following user centered design principles, we ensure user involvement in various user studies during the development process
Advances in Artificial Intelligence: Models, Optimization, and Machine Learning
The present book contains all the articles accepted and published in the Special Issue âAdvances in Artificial Intelligence: Models, Optimization, and Machine Learningâ of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications
Electronic Health Record Phenotyping in Cardiovascular Epidemiology
The secondary use of EHR data for research is a cost-effective resource for a variety of research questions and domains; however, there are many challenges when using electronic health record (EHR) data for epidemiologic research.This dissertation quantified differences in prevalence for acute myocardial infarction (MI) and heart failure (HF) using phenotyping algorithms differing in diagnosis position of ICD-10-CM codes and the inclusion of clinical components. The period of interest was January 1, 2016 to December 31, 2019 for UNC Clinical Data Warehouse for Health data and October 1, 2015 and December 31, 2019 for Atherosclerosis Risk in Communities (ARIC) Study data, the latter used for validation analyses. During the period of interest, 13,200 acute MI cases and 53,545 HF cases were identified in the UNC data. Age-standardized prevalence of acute MI and HF were highest using Any Diagnosis Position algorithm and lowest for acute MI using 1st or 2nd Diagnosis Position with Lab or Procedure and 1st Diagnosis Position for HF. Projected differences in healthcare expenditures by algorithm as well as patient and clinical characteristics, such as event severity and mortality, were also estimated. When compared to physician-adjudicated hospitalizations in the ARIC study, the phenotyping algorithms used for the UNC analysis performed well given their simplicity. The algorithm with the highest sensitivity was Any Diagnosis Position for acute MI and HF at 75.5% and 70.5%. Specificity, PPV, and NPV ranged from 80-99% for all algorithms. Requiring clinical components had little effect except for increasing PPV slightly, while restricting diagnosis position to 1st or 2nd position decreased sensitivity and increased PPV. The impact of clinical components or diagnosis position did not differ by race, age, or sex subgroups.The results from this dissertation can be used by researchers using EHR data for a variety of reasons from informing their own analytic decisions to validating their study findings. The continued use of EHR data for research requires transparency to facilitate reproducibility as well as studies focused on what we are measuring.Doctor of Philosoph
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