Classifying German Animal Experiment Summaries with Multi-lingual BERT at CLEF eHealth 2019 Task 1

Given this distribution of categories, we merged the training and the development set into one dataset that we used to train the classifiers with a k-fold cross validation..

The provided training data consists of cause-of-death texts, ICD-10 codes, and demographic information in the French, Hungarian, and Italian languages.. The data is given in either

Here, the original ICD-10 code mappings, found in the supplied dictionaries, are extended with the training instances from individual certificate data from the three

In particular, we were interested in automatically gathering medical acronyms from a Wikipedia page and manually cleaning the table of expanded acronyms (for example,

Finally, the top candidate, generated from the overlap between the list of possible ICD-10 code candidates (input list) and ranked list of final ICD-10 candidates (output list),

The requirements to the method of compiling the project execution plan for the multi-agent model are formalized and an overview of applied tasks of work planning in multi-agent

To help laypeople, Conference and Labs of the Evaluation Forum (CLEF) launched the eHealth Evaluation Lab in 2013[1].The 2016 CLEF eHealth Task 3 Patient-Centred Information

We applied the proposed approach with the optimal weights obtained from TREC 2011 and 2012 Medical Records Track into the Query Likelihood model (Run 2) and Markov Random Field