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In order to prevent such biases it is important to cross check the interview response with the document data.
Using Deep Learning techniques to extract meaningful data representations makes it possible to obtain semantic features from such high-dimensional textual data, which in turn also leads to the reduction of the dimensions of the document data representations.
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For example, only specific aspects of the documented content of communication were evaluated and the quality of communication could not always be ascertained from the documented data.
However, the documented data is controversial.
In most of these cases the documented data value did not correspond to the true lowest or highest value.
The reliability of the documented data should be questioned, because what was actually done might not have been written down.
Our objectives are therefore to develop an efficient method to document QoL during the clinical practice, make the documented data available within the CIS and use it both for clinical routine and research.
The documented data (Table 4) suggest that mutations in DCM may occur almost anywhere in LMNA; however, the Coil1b region, which is important for lamin A/C dimerization and lamin B interaction, seems to be affected most frequently.
In the main document, data are presented as choropleth maps of the WHO European region where the map categories represent rounded data quartiles.
Also, through intuitive learning process, the underlying structure of feature hierarchies was revealed for the complex document data.
This is a natural fit for the flexible document data model and enables easy extension to new log formats.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com