scholarly journals Design and Use of Semantic Resources: Findings from the Section on Knowledge Representation and Management of the 2020 International Medical Informatics Association Yearbook

2020 ◽  
Vol 29 (01) ◽  
pp. 163-168
Author(s):  
Ferdinand Dhombres ◽  
Jean Charlet ◽  
Keyword(s):  
Knowledge Representation ◽  
Medical Informatics ◽  
Large Scale ◽  
Knowledge Bases ◽  
International Medical ◽  
Biomedical Data ◽  
Semantic Representations ◽  
Provenance Information ◽  
Research Areas ◽  
Semantic Resources

Objective: To select, present, and summarize the best papers in the field of Knowledge Representation and Management (KRM) published in 2019. Methods: A comprehensive and standardized review of the biomedical informatics literature was performed to select the most interesting papers of KRM published in 2019, based on PubMed and ISI Web Of Knowledge queries. Results: Four best papers were selected among 1,189 publications retrieved, following the usual International Medical Informatics Association Yearbook reviewing process. In 2019, research areas covered by pre-selected papers were represented by the design of semantic resources (methods, visualization, curation) and the application of semantic representations for the integration/enrichment of biomedical data. Besides new ontologies and sound methodological guidance to rethink knowledge bases design, we observed large scale applications, promising results for phenotypes characterization, semantic-aware machine learning solutions for biomedical data analysis, and semantic provenance information representations for scientific reproducibility evaluation. Conclusion: In the KRM selection for 2019, research on knowledge representation demonstrated significant contributions both in the design and in the application of semantic resources. Semantic representations serve a great variety of applications across many medical domains, with actionable results.

scholarly journals Infectious Disease Research in the Era of Big Data

2020 ◽  
Vol 3 (1) ◽  
pp. 43-59
Author(s):  
Peter M. Kasson
Keyword(s):  
Infectious Disease ◽  
Large Scale ◽  
Data Science ◽  
Research Progress ◽  
Biomedical Data ◽  
Disease Research ◽  
Research Areas ◽  
Large Scale Data ◽  
Infectious Disease Research ◽  
Unique Nature

Infectious disease research spans scales from the molecular to the global—from specific mechanisms of pathogen drug resistance, virulence, and replication to the movement of people, animals, and pathogens around the world. All of these research areas have been impacted by the recent growth of large-scale data sources and data analytics. Some of these advances rely on data or analytic methods that are common to most biomedical data science, while others leverage the unique nature of infectious disease, namely its communicability. This review outlines major research progress in the past few years and highlights some remaining opportunities, focusing on data or methodological approaches particular to infectious disease.

scholarly journals Efficient Results in Semantic Interoperability for Health Care

2016 ◽  
Vol 25 (01) ◽  
pp. 184-187
Author(s):  
J. Charlet ◽  
L. F. Soualmia ◽  
Keyword(s):  
Health Care ◽  
Semantic Web ◽  
Knowledge Representation ◽  
Large Scale ◽  
Recommendation System ◽  
Clinical Care ◽  
Knowledge Bases ◽  
Semantic Interoperability ◽  
The Third ◽  
Research Activities

Summary Objectives: To summarize excellent current research in the field of Knowledge Representation and Management (KRM) within the health and medical care domain. Method: We provide a synopsis of the 2016 IMIA selected articles as well as a related synthetic overview of the current and future field activities. A first step of the selection was performed through MEDLINE querying with a list of MeSH descriptors completed by a list of terms adapted to the KRM section. The second step of the selection was completed by the two section editors who separately evaluated the set of 1,432 articles. The third step of the selection consisted of a collective work that merged the evaluation results to retain 15 articles for peer-review. Results: The selection and evaluation process of this Yearbook’s section on Knowledge Representation and Management has yielded four excellent and interesting articles regarding semantic interoperability for health care by gathering heterogeneous sources (knowledge and data) and auditing ontologies. In the first article, the authors present a solution based on standards and Semantic Web technologies to access distributed and heterogeneous datasets in the domain of breast cancer clinical trials. The second article describes a knowledge-based recommendation system that relies on ontologies and Semantic Web rules in the context of chronic diseases dietary. The third article is related to concept-recognition and text-mining to derive common human diseases model and a phenotypic network of common diseases. In the fourth article, the authors highlight the need for auditing the SNOMED CT. They propose to use a crowd-based method for ontology engineering. Conclusions: The current research activities further illustrate the continuous convergence of Knowledge Representation and Medical Informatics, with a focus this year on dedicated tools and methods to advance clinical care by proposing solutions to cope with the problem of semantic interoperability. Indeed, there is a need for powerful tools able to manage and interpret complex, large-scale and distributed datasets and knowledge bases, but also a need for user-friendly tools developed for the clinicians in their daily practice.

COMBINING DEFAULT LOGIC DATABASES

1994 ◽  
Vol 03 (03) ◽  
pp. 319-348 ◽  
Author(s):  
CHITTA BARAL ◽  
SARIT KRAUS ◽  
JACK MINKER ◽  
V. S. SUBRAHMANIAN
Keyword(s):  
Knowledge Representation ◽  
Knowledge Base ◽  
Large Scale ◽  
Future Generation ◽  
Knowledge Bases ◽  
Default Logic ◽  
Integrity Constraints ◽  
Specialized Knowledge ◽  
The Past ◽  
The Future

During the past decade, it has become increasingly clear that the future generation of large-scale knowledge bases will consist, not of one single isolated knowledge base, but a multiplicity of specialized knowledge bases that contain knowledge about different domains of expertise. These knowledge bases will work cooperatively, pooling together their varied bodies of knowledge, so as to be able to solve complex problems that no single knowledge base, by itself, would have been able to address successfully. In any such situation, inconsistencies are bound to arise. In this paper, we address the question: "Suppose we have a set of knowledge bases, KB1, …, KBn, each of which uses default logic as the formalism for knowledge representation, and a set of integrity constraints IC. What knowledge base constitutes an acceptable combination of KB1, …, KBn?"

scholarly journals Knowledge Representation and Management: Interest in New Solutions for Ontology Curation

2021 ◽  
Vol 30 (01) ◽  
pp. 185-190
Author(s):  
Ferdinand Dhombres ◽  
Jean Charlet ◽  
Keyword(s):  
Machine Learning ◽  
Electronic Health Records ◽  
Knowledge Representation ◽  
Medical Informatics ◽  
Open Science ◽  
Research Interest ◽  
Health Records ◽  
Knowledge Representations ◽  
Research Areas ◽  
Significant Research

Summary Objective: To select, present and summarize some of the best papers in the field of Knowledge Representation and Management (KRM) published in 2020. Methods: A comprehensive and standardized review of the medical informatics literature was performed to select the most interesting papers of KRM published in 2020, based on PubMed queries. This review was conducted according to the IMIA Yearbook guidelines. Results: Four best papers were selected among 1,175 publications. In contrast with the papers selected last year, the four best papers of 2020 demonstrated a significant focus on methods and tools for ontology curation and design. The usual KRM application domains (bioinformatics, machine learning, and electronic health records) were also represented. Conclusion: In 2020, ontology curation emerges as a significant topic of research interest. Bioinformatics, machine learning, and electronics health records remain significant research areas in the KRM community with various applications. Knowledge representations are key to advance machine learning by providing context and to develop novel bioinformatics metrics. As in 2019, representations serve a great variety of applications across many medical domains, with actionable results and now with growing adhesion to the open science initiative.

scholarly journals As Ontologies Reach Maturity, Artificial Intelligence Starts Being Fully Efficient: Findings from the Section on Knowledge Representation and Management for the Yearbook 2018

2018 ◽  
Vol 27 (01) ◽  
pp. 140-145 ◽  
Author(s):  
Ferdinand Dhombres ◽  
Jean Charlet ◽  
Keyword(s):  
Knowledge Representation ◽  
Medical Informatics ◽  
Semantic Representation ◽  
Semantic Annotation ◽  
Knowledge Bases ◽  
Medical Data ◽  
Added Value ◽  
Association Mining ◽  
Clinical Practices ◽  
New Challenges

Objectives: To select, present, and summarize the best papers published in 2017 in the field of Knowledge Representation and Management (KRM). Methods: A comprehensive and standardized review of the medical informatics literature was performed to select the most interesting papers of KRM published in 2017, based on a PubMed query. Results: In direct line with the research on data integration presented in the KRM section of the 2017 edition of the International Medical Informatics Association (IMIA) Yearbook, the five best papers for 2018 demonstrate even further the added-value of ontology-based integration approaches for phenotype-genotype association mining. Additionally, among the 15 preselected papers, two aspects of KRM are in the spotlight: the design of knowledge bases and new challenges in using ontologies. Conclusions: Ontologies are demonstrating their maturity to integrate medical data and begin to support clinical practices. New challenges have emerged: the query on distributed semantically annotated datasets, the efficiency of semantic annotation processes, the semantic representation of large textual datasets, the control of biases associated with semantic annotations, and the computation of Bayesian indicators on data annotated with ontologies.

A Bio-Medical Informatics Perspective on Human Factors

2008 ◽  
Vol 17 (01) ◽  
pp. 41-43
Author(s):  
P. Ruch ◽  
Keyword(s):  
Human Factors ◽  
Interpersonal Communication ◽  
Medical Informatics ◽  
Large Scale ◽  
Biomedical Data ◽  
Scientific Investigations ◽  
Working Environments ◽  
Excellent Research ◽  
Medical Order ◽  
High Throughput Experiments

Summary Objectives To summarize current excellent research in the field of human factors. MethodsWe provide a synopsis of the articles selected for the IMIA Yearbook 2008, from which we attempt to derive a synthetic overview of the activity and new trends in the field. Results while the state of the research in the field of human factors is illustrated by a set of fairly heterogeneous studies, it is possible to identify trends. Thus, clearly, the importance of issues related to medical order entry, which also founded human factors studies in medical informatics, still occupies a central role in the field. In parallel, we observe an emerging interest for human factors from the field of bioinformatics, where the mass of data generated by high/ throughput experiments and large-scale genome analysis projects, raises specific processing challenges. Such challenges will have to be addressed to achieve post-genomics era medicine. Conclusions The best paper selection of articles on human factors shows examples of excellent research on methods concerning original development as well as quality assurance of previously reported studies. The crucial role of preserving interpersonal communication among healthcare staff in computerized working environments is complemented by more original scientific investigations, which demonstrate the needs for computerized applications to transform the biomedical data overflow into more operational clinical knowledge. Altogether these papers support the idea that more elaborated computer tools, likely to combine contextual contents, are needed.

scholarly journals Formal Medical Knowledge Representation Supports Deep Learning Algorithms, Bioinformatics Pipelines, Genomics Data Analysis, and Big Data Processes

2019 ◽  
Vol 28 (01) ◽  
pp. 152-155
Author(s):  
Ferdinand Dhombres ◽  
Jean Charlet ◽  
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Knowledge Representation ◽  
Semantic Annotation ◽  
Medical Knowledge ◽  
Added Value ◽  
Association Mining ◽  
Learning Approaches ◽  
Semantic Representations ◽  
Research Areas

Objective: To select, present, and summarize the best papers published in 2018 in the field of Knowledge Representation and Management (KRM). Methods: A comprehensive and standardized review of the medical informatics literature was performed to select the most interesting papers published in 2018 in KRM, based on PubMed and ISI Web Of Knowledge queries. Results: Four best papers were selected among the 962 publications retrieved following the Yearbook review process. The research areas in 2018 were mainly related to the ontology-based data integration for phenotype-genotype association mining, the design of ontologies and their application, and the semantic annotation of clinical texts. Conclusion: In the KRM selection for 2018, research on semantic representations demonstrated their added value for enhanced deep learning approaches in text mining and for designing novel bioinformatics pipelines based on graph databases. In addition, the ontology structure can enrich the analyses of whole genome expression data. Finally, semantic representations demonstrated promising results to process phenotypic big data.

Health and Medical Informatics Competencies: Call to Participate in Updating the IMIA Recommendations

2002 ◽  
Vol 41 (02) ◽  
pp. 86-88 ◽  
Author(s):  
Judith Douglas ◽  
Evelyn Hovenga
Keyword(s):  
Literature Review ◽  
Small Group ◽  
Learning Outcomes ◽  
Medical Informatics ◽  
Working Group ◽  
International Medical ◽  
Three Dimension ◽  
Virtual University ◽  
Group 1

Summary Objectives: On behalf of the International Medical Informatics Association (IMIA), its Working Group 1 (WG1) addresses health and medical informatics education. Methods: As part of its mission, WG1 developed recommendations for competencies, describing a three-dimension framework and defining learning outcomes. Results: Officially approved by IMIA in 1999, the recommendations have been translated into seven languages. In 2001, WG1 charged a small group with updating the recommendations and consider the work undertaken by others to develop competencies. Additional work underway in support of the recommendations includes a literature review to help extract the fundamental competencies from the recommendations. To ensure the highest quality of input in the updated recommendations, WG1 is issuing a call for participation to the international informatics community. Conclusions: Further work with the competencies will result in updated IMIA guidelines. These are expected to support the creation of a virtual university for health and medical informatics.

The long-term development of Russian biotech sector

foresight ◽  
2017 ◽  
Vol 19 (5) ◽  
pp. 491-500 ◽  
Author(s):  
Anna Grebenyuk ◽  
Nikolai Ravin
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Technological Development ◽  
Science And Technology ◽  
Plant Biotechnology ◽  
High Tech ◽  
Content Type ◽  
Research Areas ◽  
Research Activities ◽  
The Usa

Purpose To define strategic directions for the Russia’s social, economic, scientific and technological development in 2011-2013, a large-scale foresight study including the deep analysis of prospects of biotechnology development there was undertaken (Russia 2030: Science and Technology Foresight). This paper aims to present results of this research. Design/methodology/approach The study was based on a combination of technology-push and market-pull approaches that aimed not only to identify most promising science and technology (S&T) areas but also to understand how they can be realized in practice. Representatives from federal authorities, science and business were involved in the project to create future visions of technological directions; analyze grand challenges, weak signals and wild cards; and set research and development (R&D) priorities. Findings According to results of the study, Russia has a potential for biotech sector development, although the level of R&D in the majority of areas is lagging behind that in the USA and leading EU countries. However, there are several advanced applied research areas where efforts can be focused. Among them are high-performance genomics and post-genomics research platforms, systems and structural biology, microbial metabolic engineering, plant biotechnology and microbial strains and consortia for development of symbiotic plant–microbial communities. Originality/value Concentration of available resources of government and business on biotechnological sector development can help to find answers for challenges that Russia faces today or will face tomorrow. It will help to pick up on the current level of research activities, improve the quality of personnel training, make this area the engine of the economy and carry out the so-called new industrialization of the country, building a new, high-tech device industry.

scholarly journals Evapotranspiration Estimation with Small UAVs in Precision Agriculture

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6427
Author(s):  
Haoyu Niu ◽  
Derek Hollenbeck ◽  
Tiebiao Zhao ◽  
Dong Wang ◽  
YangQuan Chen
Keyword(s):  
Energy Balance ◽  
High Resolution ◽  
Spatial Resolution ◽  
Precision Agriculture ◽  
Large Scale ◽  
Bowen Ratio ◽  
Future Research ◽  
Accurate Estimation ◽  
Research Areas ◽  
Energy Balance Approach

Estimating evapotranspiration (ET) has been one of the most critical research areas in agriculture because of water scarcity, the growing population, and climate change. The accurate estimation and mapping of ET are necessary for crop water management. Traditionally, researchers use water balance, soil moisture, weighing lysimeters, or an energy balance approach, such as Bowen ratio or eddy covariance towers to estimate ET. However, these ET methods are point-specific or area-weighted measurements and cannot be extended to a large scale. With the advent of satellite technology, remote sensing images became able to provide spatially distributed measurements. However, the spatial resolution of multispectral satellite images is in the range of meters, tens of meters, or hundreds of meters, which is often not enough for crops with clumped canopy structures, such as trees and vines. Unmanned aerial vehicles (UAVs) can mitigate these spatial and temporal limitations. Lightweight cameras and sensors can be mounted on the UAVs and take high-resolution images. Unlike satellite imagery, the spatial resolution of the UAV images can be at the centimeter-level. UAVs can also fly on-demand, which provides high temporal imagery. In this study, the authors examined different UAV-based approaches of ET estimation at first. Models and algorithms, such as mapping evapotranspiration at high resolution with internalized calibration (METRIC), the two-source energy balance (TSEB) model, and machine learning (ML) are analyzed and discussed herein. Second, challenges and opportunities for UAVs in ET estimation are also discussed, such as uncooled thermal camera calibration, UAV image collection, and image processing. Then, the authors share views on ET estimation with UAVs for future research and draw conclusive remarks.

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