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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
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GraSCCo

Authors: Schulz, Stefan;
Abstract

GraSCCo - Graz Synthetic Clinical text Corpus GraSCCo is a collection of artificially generated semi-structured and unstructured German-language clinical summaries. These summaries are formulated as letters from the hospital to the patient's GP after in-patient or out-patient care. This is common practice in Germany, Austria and Switzerland. The creation of the GraSCCo documents were inspired by existing clinical texts, but all names and dates are purely fictional. There is no relation to existing patients, clinicians or institutions. Whereas the texts try to represent the range of German clinical language as best as possible, medical plausibility must not be assumed. GraSCCo can therefore only be used to train clinical language models, not clinical domain models. Creator: Stefan Schulz, steschu@gmail.com, stefan.schulz@medunigraz.at No copyright (Creative Commons "CC0"): The author, Stefan Schulz, has dedicated the work to the public domain by waiving all of his rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law. Creative Commons — CC0 1.0 Universal Publication: Modersohn L, Schulz S, Lohr C and Hahn U: GraSCCo — The First Publicly Shareable, Multiply-Alienated German Clinical Text Corpus; GMDS 2022 (accepted)

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Keywords

Clinical NLP, Case reports, German Clinical Document Corpus

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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