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Charlet J, Cui L. Knowledge Representation and Management 2022: Findings in Ontology Development and Applications. Yearb Med Inform 2023; 32:225-229. [PMID: 38147864 PMCID: PMC10751114 DOI: 10.1055/s-0043-1768747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023] Open
Abstract
OBJECTIVES To select, present, and summarize the best papers in 2022 for the Knowledge Representation and Management (KRM) section of the International Medical Informatics Association (IMIA) Yearbook. METHODS We conducted PubMed queries and followed the IMIA Yearbook guidelines for performing biomedical informatics literature review to select the best papers in KRM published in 2022. RESULTS We retrieved 1,847 publications from PubMed. We nominated 15 candidate best papers, and two of them were finally selected as the best papers in the KRM section. The topics covered by the candidate papers include ontology and knowledge graph creation, ontology applications, ontology quality assurance, ontology mapping standard, and conceptual model. CONCLUSIONS In the KRM best paper selection for 2022, the candidate best papers encompassed a broad range of topics, with ontology and knowledge graph creation remaining a considerable research focus.
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Affiliation(s)
- Jean Charlet
- Sorbonne Université, INSERM, Univ Sorbonne Paris Nord, LIMICS, Paris, France
- AP-HP, DRCI, Paris, France
| | - Licong Cui
- McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
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Kharazmi E, Ostovar S, Ahmadi Marzaleh M. The need to reorganize health research systems in pandemic crisis: A prospective study. Health Sci Rep 2023; 6:e1146. [PMID: 36925765 PMCID: PMC10011385 DOI: 10.1002/hsr2.1146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 02/06/2023] [Accepted: 02/22/2023] [Indexed: 03/16/2023] Open
Abstract
Background and Aims A pandemic has posed a major challenge to health systems all over the world. All countries have realized that the only way to get real growth and development and solve their problems is to use what they have learned from research. Methods A descriptive and analytic type of study was conducted with the help of experts in the field of health research. The components affecting the research system were obtained via process approach and content analysis methods, and then the position of each component was identified by the Mic Mac technique. Results Seventeen influential structural components in the health research system were identified. The leadership and management components had the most direct and indirect influence among other components. The health promotion component had a greater dependency than the other components. Conclusion All health systems need to provide adequate financial resources and manpower to provide a useful research system. Human resources are the most important inputs to such a system. Components such as the research process, research sustainability, quality, or innovation in research can play a balancing role. Having the right infrastructures for creating, transferring, developing, and getting access to knowledge makes it possible to do systematic science. It is hoped that this science will be used in other results of the health research system, like improving the effectiveness or promoting health.
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Affiliation(s)
- Erfan Kharazmi
- Department of Healthcare Services Management, Health Human Resources Research Center, School of Health Management and Information Sciences Shiraz University of Medical Sciences Shiraz Iran
| | | | - Milad Ahmadi Marzaleh
- Department of Health in Disasters and Emergencies, School of Health Management and Information Sciences Shiraz University of Medical Sciences Shiraz Iran
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Abstract
OBJECTIVES To select, present, and summarize the best papers in the field of Knowledge Representation and Management (KRM) published in 2021. METHODS Following the International Medical Informatics Association (IMIA) Yearbook guidelines, a comprehensive and standardized review of the biomedical informatics literature was performed to select the best KRM papers published in 2021, based on PubMed queries. RESULTS A total of 1,231 publications were retrieved from PubMed. We nominated 15 candidate best papers, and four of them were finally selected as the best papers in the KRM section. The topics covered by these papers include knowledge graph, ontology development, ontology alignment, and the International Classification of Diseases. CONCLUSION In the KRM best paper selection for 2021, the candidate best papers covered a wider spectrum of topics compared to the last year's significant focus on ontology curation. In particular, ontology development for specific domains (e.g., Alzheimer's disease, infectious diseases, bioethics) has received the most attention.
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Affiliation(s)
- Licong Cui
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA,Correspondence to: Licong Cui School of Biomedical Informatics, The University of Texas Health Science Center at Houston7000 Fannin Street Houston, TX 77030USA
| | - Ferdinand Dhombres
- Sorbonne Université, INSERM, Univ Sorbonne Paris Nord, LIMICS, Paris, France,Sorbonne Université, Service de Médecine Foetale, DMU Origyne, AP-HP, Hôpital Armand Trousseau, Paris, France
| | - Jean Charlet
- Sorbonne Université, INSERM, Univ Sorbonne Paris Nord, LIMICS, Paris, France,AP-HP, DRCI, Paris, France
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Abstract
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.
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Affiliation(s)
- Ferdinand Dhombres
- Sorbonne Université, INSERM, Univ Sorbonne Paris Nord, LIMICS, Paris, France.,Sorbonne Université, Service de Médecine Fœtale, DMU Origyne, AP-HP, Hôpital Armand Trousseau, Paris, France
| | - Jean Charlet
- Sorbonne Université, INSERM, Univ Sorbonne Paris Nord, LIMICS, Paris, France.,AP-HP, DRCI, Paris, France
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Dhombres F, Charlet J. Design and Use of Semantic Resources: Findings from the Section on Knowledge Representation and Management of the 2020 International Medical Informatics Association Yearbook. Yearb Med Inform 2020; 29:163-168. [PMID: 32823311 PMCID: PMC7442529 DOI: 10.1055/s-0040-1702010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
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.
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Affiliation(s)
- Ferdinand Dhombres
- Sorbonne Université, Université Paris Nord, INSERM, UMR_S 1142, LIMICS, Paris, France
- Médecine Sorbonne Université, Service de Médecine Fœtale, Hôpital Armand Trousseau, Paris, France
| | - Jean Charlet
- Sorbonne Université, Université Paris Nord, INSERM, UMR_S 1142, LIMICS, Paris, France
- AP-HP, DRCI, Paris, France
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Harrow I, Balakrishnan R, Jimenez-Ruiz E, Jupp S, Lomax J, Reed J, Romacker M, Senger C, Splendiani A, Wilson J, Woollard P. Ontology mapping for semantically enabled applications. Drug Discov Today 2019; 24:2068-75. [PMID: 31158512 DOI: 10.1016/j.drudis.2019.05.020] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Revised: 04/12/2019] [Accepted: 05/28/2019] [Indexed: 12/14/2022]
Abstract
In this review, we provide a summary of recent progress in ontology mapping (OM) at a crucial time when biomedical research is under a deluge of an increasing amount and variety of data. This is particularly important for realising the full potential of semantically enabled or enriched applications and for meaningful insights, such as drug discovery, using machine-learning technologies. We discuss challenges and solutions for better ontology mappings, as well as how to select ontologies before their application. In addition, we describe tools and algorithms for ontology mapping, including evaluation of tool capability and quality of mappings. Finally, we outline the requirements for an ontology mapping service (OMS) and the progress being made towards implementation of such sustainable services.
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Doing-Harris K, Bray BE, Thackeray A, Shah RU, Shao Y, Cheng Y, Zeng-Treitler Q, Garvin JH, Weir C. Development of a cardiac-centered frailty ontology. J Biomed Semantics 2019; 10:3. [PMID: 30658684 PMCID: PMC6339414 DOI: 10.1186/s13326-019-0195-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Accepted: 01/01/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND A Cardiac-centered Frailty Ontology can be an important foundation for using NLP to assess patient frailty. Frailty is an important consideration when making patient treatment decisions, particularly in older adults, those with a cardiac diagnosis, or when major surgery is a consideration. Clinicians often report patient's frailty in progress notes and other documentation. Frailty is recorded in many different ways in patient records and many different validated frailty-measuring instruments are available, with little consistency across instruments. We specifically explored concepts relevant to decisions regarding cardiac interventions. We based our work on text found in a large corpus of clinical notes from the Department of Veterans Affairs (VA) national Electronic Health Record (EHR) database. RESULTS The full ontology has 156 concepts, with 246 terms. It includes 86 concepts we expect to find in clinical documents, with 12 qualifier values. The remaining 58 concepts represent hierarchical groups (e.g., physical function findings). Our top-level class is clinical finding, which has children clinical history finding, instrument finding, and physical examination finding, reflecting the OGMS definition of clinical finding. Instrument finding is any score found for the existing frailty instruments. Within our ontology, we used SNOMED-CT concepts where possible. Some of the 86 concepts we expect to find in clinical documents are associated with the properties like ability interpretation. The concept ability to walk can either be able, assisted or unable. Each concept-property level pairing gets a different frailty score. Each scored concept received three scores: a frailty score, a relevance to cardiac decisions score, and a likelihood of resolving after the recommended intervention score. The ontology includes the relationship between scores from ten frailty instruments and frailty as assessed using ontology concepts. It also included rules for mapping ontology elements to instrument items for three common frailty assessment instruments. Ontology elements are used in two clinical NLP systems. CONCLUSIONS We developed and validated a Cardiac-centered Frailty Ontology, which is a machine-interoperable description of frailty that reflects all the areas that clinicians consider when deciding which cardiac intervention will best serve the patient as well as frailty indications generally relevant to medical decisions. The ontology owl file is available on Bioportal at http://bioportal.bioontology.org/ontologies/CCFO .
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Affiliation(s)
| | - Bruce E. Bray
- Division of Cardiovascular Medicine, University of Utah, Salt Lake City, UT USA
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT USA
| | - Anne Thackeray
- Physical Therapy and Athletic Training Department, University of Utah, Salt Lake City, UT USA
| | - Rashmee U. Shah
- Division of Cardiovascular Medicine, University of Utah, Salt Lake City, UT USA
| | - Yijun Shao
- Medical Informatics Center, George Washington University, Washington DC, USA
| | - Yan Cheng
- Medical Informatics Center, George Washington University, Washington DC, USA
| | - Qing Zeng-Treitler
- Medical Informatics Center, George Washington University, Washington DC, USA
| | - Jennifer H. Garvin
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT USA
- VA Healthcare System, Salt Lake City, UT USA
| | - Charlene Weir
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT USA
- VA Healthcare System, Salt Lake City, UT USA
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Dhombres F, Charlet J. As Ontologies Reach Maturity, Artificial Intelligence Starts Being Fully Efficient: Findings from the Section on Knowledge Representation and Management for the Yearbook 2018. Yearb Med Inform 2018; 27:140-145. [PMID: 30157517 PMCID: PMC6115232 DOI: 10.1055/s-0038-1667078] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
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.
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Affiliation(s)
- Ferdinand Dhombres
- Sorbonne Université, Université Paris 13, Sorbonne Paris Cité, INSERM, UMR_S 1142, LIMICS, Paris, France.,Sorbonne Université Médecine, Service de Médecine Foetale, AP-HP/HUEP, Hôpital Armand Trousseau, Paris, France
| | - Jean Charlet
- Sorbonne Université, Université Paris 13, Sorbonne Paris Cité, INSERM, UMR_S 1142, LIMICS, Paris, France.,AP-HP, DRCI, Paris, France
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Zhang H, Guo Y, Li Q, George TJ, Shenkman E, Modave F, Bian J. An ontology-guided semantic data integration framework to support integrative data analysis of cancer survival. BMC Med Inform Decis Mak 2018; 18:41. [PMID: 30066664 PMCID: PMC6069766 DOI: 10.1186/s12911-018-0636-4] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Cancer is the second leading cause of death in the United States, exceeded only by heart disease. Extant cancer survival analyses have primarily focused on individual-level factors due to limited data availability from a single data source. There is a need to integrate data from different sources to simultaneously study as much risk factors as possible. Thus, we proposed an ontology-based approach to integrate heterogeneous datasets addressing key data integration challenges. METHODS Following best practices in ontology engineering, we created the Ontology for Cancer Research Variables (OCRV) adapting existing semantic resources such as the National Cancer Institute (NCI) Thesaurus. Using the global-as-view data integration approach, we created mapping axioms to link the data elements in different sources to OCRV. Implemented upon the Ontop platform, we built a data integration pipeline to query, extract, and transform data in relational databases using semantic queries into a pooled dataset according to the downstream multi-level Integrative Data Analysis (IDA) needs. RESULTS Based on our use cases in the cancer survival IDA, we created tailored ontological structures in OCRV to facilitate the data integration tasks. Specifically, we created a flexible framework addressing key integration challenges: (1) using a shared, controlled vocabulary to make data understandable to both human and computers, (2) explicitly modeling the semantic relationships makes it possible to compute and reason with the data, (3) linking patients to contextual and environmental factors through geographic variables, (4) being able to document the data manipulation and integration processes clearly in the ontologies. CONCLUSIONS Using an ontology-based data integration approach not only standardizes the definitions of data variables through a common, controlled vocabulary, but also makes the semantic relationships among variables from different sources explicit and clear to all users of the same datasets. Such an approach resolves the ambiguity in variable selection, extraction and integration processes and thus improve reproducibility of the IDA.
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Affiliation(s)
- Hansi Zhang
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Clinical and Translational Research Building Suite 3228, 2004 Mowry Road, PO Box 100219, Gainesville, FL, 32610-0219, USA
| | - Yi Guo
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Clinical and Translational Research Building Suite 3228, 2004 Mowry Road, PO Box 100219, Gainesville, FL, 32610-0219, USA
| | - Qian Li
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Clinical and Translational Research Building Suite 3228, 2004 Mowry Road, PO Box 100219, Gainesville, FL, 32610-0219, USA
| | - Thomas J George
- Division of Hematology and Oncology, Department of Medicine, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Elizabeth Shenkman
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Clinical and Translational Research Building Suite 3228, 2004 Mowry Road, PO Box 100219, Gainesville, FL, 32610-0219, USA
| | - François Modave
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Clinical and Translational Research Building Suite 3228, 2004 Mowry Road, PO Box 100219, Gainesville, FL, 32610-0219, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Clinical and Translational Research Building Suite 3228, 2004 Mowry Road, PO Box 100219, Gainesville, FL, 32610-0219, USA.
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