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    • Natural-language and speech processing; Ontologies, knowledge bases, data models and metadata; Knowledge acquisition and processing; MEDINFO, 2015 (2)
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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 5 of 5 1 IMIA LaMB WG publications: Medical Concept Representation ‐ the Years Beyond 2000 ... Balkanyi, Laszlo; Schulz, Stefan; Cornet, Ronald. - : Zenodo, 2019 BASE Show details 2 IMIA LaMB WG publications: Medical Concept Representation ‐ the Years Beyond 2000 ... Balkanyi, Laszlo; Schulz, Stefan; Cornet, Ronald. - : Zenodo, 2019 BASE Show details 3 IMIA LaMB WG publications: 'Biomedical Semantics in the Big Data Era' - conference paper introducing the related workshop, MEDINFO, 2015 ... Cornet, Ronald; Balkanyi, Laszlo; Bodenreider, Olivier. - : Zenodo, 2019 BASE Show details 4 IMIA LaMB WG publications: 'Biomedical Semantics in the Big Data Era' - conference paper introducing the related workshop, MEDINFO, 2015 ... Cornet, Ronald; Balkanyi, Laszlo; Bodenreider, Olivier. - : Zenodo, 2019 BASE Show details 5 The Interplay of Knowledge Representation with Various Fields of Artificial Intelligence in Medicine: A Contribution from the IMIA Working Group on Language and Meaning in BioMedicine Balkanyi, Laszlo; Cornet, Ronald. - : Georg Thieme Verlag KG, 2019 Abstract: Introduction : Artificial intelligence (AI) is widespread in many areas, including medicine. However, it is unclear what exactly AI encompasses. This paper aims to provide an improved understanding of medical AI and its constituent fields, and their interplay with knowledge representation (KR). Methods : We followed a Wittgensteinian approach (“meaning by usage”) applied to content metadata labels, using the Medical Subject Headings (MeSH) thesaurus to classify the field. To understand and characterize medical AI and the role of KR, we analyzed: (1) the proportion of papers in MEDLINE related to KR and various AI fields; (2) the interplay among KR and AI fields and overlaps among the AI fields; (3) interconnectedness of fields; and (4) phrase frequency and collocation based on a corpus of abstracts. Results : Data from over eighty thousand papers showed a steep, six-fold surge in the last 30 years. This growth happened in an escalating and cascading way. A corpus of 246,308 total words containing 21,842 unique words showed several hundred occurrences of notions such as robotics, fuzzy logic, neural networks, machine learning and expert systems in the phrase frequency analysis. Collocation analysis shows that fuzzy logic seems to be the most often collocated notion. Neural networks and machine learning are also used in the conceptual neighborhood of KR. Robotics is more isolated. Conclusions : Authors note an escalation of published AI studies in medicine. Knowledge representation is one of the smaller areas, but also the most interconnected, and provides a common cognitive layer for other areas. URL: https://doi.org/10.1055/s-0039-1677899 http://www.ncbi.nlm.nih.gov/pubmed/31022748 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697493/ BASE Hide details

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