32,918 research outputs found
Barry Smith an sich
Festschrift in Honor of Barry Smith on the occasion of his 65th Birthday. Published as issue 4:4 of the journal Cosmos + Taxis: Studies in Emergent Order and Organization. Includes contributions by Wolfgang Grassl, Nicola Guarino, John T. Kearns, Rudolf Lüthe, Luc Schneider, Peter Simons, Wojciech Żełaniec, and Jan Woleński
Towards new information resources for public health: From WordNet to MedicalWordNet
In the last two decades, WORDNET has evolved as the most comprehensive computational lexicon of general English. In this article, we discuss its potential for supporting the creation of an entirely new kind of information resource for public health, viz. MEDICAL WORDNET. This resource is not to be conceived merely as a lexical extension of the original WORDNET to medical terminology; indeed, there is already a considerable degree of overlap between WORDNET and the vocabulary of medicine. Instead, we propose a new type of repository, consisting of three large collections of (1) medically relevant word forms, structured along the lines of the existing Princeton WORDNET; (2) medically validated propositions, referred to here as medical facts, which will constitute what we shall call MEDICAL FACTNET; and (3) propositions reflecting laypersons’ medical beliefs, which will constitute what we shall call the MEDICAL BELIEFNET. We introduce a methodology for setting up the MEDICAL WORDNET. We then turn to the discussion of research challenges that have to be met in order to build this new type of information resource
The journals of importance to UK clinicians: A questionnaire survey of surgeons
Background: Peer-reviewed journals are seen as a major vehicle in the transmission of research
findings to clinicians. Perspectives on the importance of individual journals vary and the use of
impact factors to assess research is criticised. Other surveys of clinicians suggest a few key journals
within a specialty, and sub-specialties, are widely read. Journals with high impact factors are not
always widely read or perceived as important. In order to determine whether UK surgeons
consider peer-reviewed journals to be important information sources and which journals they read
and consider important to inform their clinical practice, we conducted a postal questionnaire
survey and then compared the findings with those from a survey of US surgeons.
Methods: A questionnaire survey sent to 2,660 UK surgeons asked which information sources
they considered to be important and which peer-reviewed journals they read, and perceived as
important, to inform their clinical practice. Comparisons were made with numbers of UK NHSfunded
surgery publications, journal impact factors and other similar surveys.
Results: Peer-reviewed journals were considered to be the second most important information
source for UK surgeons. A mode of four journals read was found with academics reading more
than non-academics. Two journals, the BMJ and the Annals of the Royal College of Surgeons of England,
are prominent across all sub-specialties and others within sub-specialties. The British Journal of
Surgery plays a key role within three sub-specialties. UK journals are generally preferred and
readership patterns are influenced by membership journals. Some of the journals viewed by
surgeons as being most important, for example the Annals of the Royal College of Surgeons of England,
do not have high impact factors.
Conclusion: Combining the findings from this study with comparable studies highlights the
importance of national journals and of membership journals. Our study also illustrates the
complexity of the link between the impact factors of journals and the importance of the journals
to clinicians. This analysis potentially provides an additional basis on which to assess the role of
different journals, and the published output from research
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
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