32,050 research outputs found
BioGUID: resolving, discovering, and minting identifiers for biodiversity informatics
Background: Linking together the data of interest to biodiversity researchers (including specimen records, images, taxonomic names, and DNA sequences) requires services that can mint, resolve, and discover globally unique identifiers (including, but not limited to, DOIs, HTTP URIs, and LSIDs).
Results: BioGUID implements a range of services, the core ones being an OpenURL resolver for bibliographic resources, and a LSID resolver. The LSID resolver supports Linked Data-friendly resolution using HTTP 303 redirects and content negotiation. Additional services include journal ISSN look-up, author name matching, and a tool to monitor the status of biodiversity data providers.
Conclusion: BioGUID is available at http://bioguid.info/. Source code is available from http://code.google.com/p/bioguid/
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
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
Multimodal Machine Learning for Automated ICD Coding
This study presents a multimodal machine learning model to predict ICD-10
diagnostic codes. We developed separate machine learning models that can handle
data from different modalities, including unstructured text, semi-structured
text and structured tabular data. We further employed an ensemble method to
integrate all modality-specific models to generate ICD-10 codes. Key evidence
was also extracted to make our prediction more convincing and explainable. We
used the Medical Information Mart for Intensive Care III (MIMIC -III) dataset
to validate our approach. For ICD code prediction, our best-performing model
(micro-F1 = 0.7633, micro-AUC = 0.9541) significantly outperforms other
baseline models including TF-IDF (micro-F1 = 0.6721, micro-AUC = 0.7879) and
Text-CNN model (micro-F1 = 0.6569, micro-AUC = 0.9235). For interpretability,
our approach achieves a Jaccard Similarity Coefficient (JSC) of 0.1806 on text
data and 0.3105 on tabular data, where well-trained physicians achieve 0.2780
and 0.5002 respectively.Comment: Machine Learning for Healthcare 201
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