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

Abstract

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.

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

“…In direct line with the research presented last year 1 , the four best papers published in 2019 demonstrated even further the added-value of ontology-based data integration approaches and that the development of ontology methods is an active area of bioinformatics research.…”

“…We conducted the selection of KRM papers based on the set of queries established in the 2019 edition of the IMIA Yearbook of Medical Informatics 1 . As compared with the previous editions of the IMIA Yearbook in 2017 and 2018 2 3 , both PubMed/MELDINE and Web of Knowledge were used to search for KRM articles published in 2019.…”

Biomedical Ontologies to Guide AI Development in Radiology

“…For analysis of radiology reports and other textual information in the electronic health record, ontologies provide a rich source of synonyms, and allow one to capture relevant features at various levels of generalization. Ontologies promote integration and interoperability among clinical, and "omics" data, and support deep learning algorithms, bioinformatics pipelines, big data analyses, and quality assurance and safety initiatives [47,48]. Knowledge resources such as biomedical ontologies are poised to help guide large-scale foundational and translational research endeavors in radiology AI [49,50].…”

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

“…We conducted the selection of KRM papers, based on the set of queries optimized during the last editions of the International Medical Informatics (IMIA) Yearbook [ 1 2 3 4 ]. In comparison with the latest editions of the IMIA Yearbook [ 3 , 4 ], where both PubMed/MELDINE and Web of Science™ were used to search for KRM articles, we did not expand the search to the Web of Science (WoS) database this year.…”

“…We conducted the selection of KRM papers, based on the set of queries optimized during the last editions of the International Medical Informatics (IMIA) Yearbook [ 1 2 3 4 ]. In comparison with the latest editions of the IMIA Yearbook [ 3 , 4 ], where both PubMed/MELDINE and Web of Science™ were used to search for KRM articles, we did not expand the search to the Web of Science (WoS) database this year. We have observed limited additional contributions in WoS in 2018 (3.4%, n=34/962) and 2019 (1.5%, n=18/1189) and no candidate paper was selected among these additional contributions, mostly because they were more bioinformatics-related than actual KRM papers.…”