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\begin{document}

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\title{WBI at CLEF eHealth 2018 Task 1: Language-independent ICD-10 coding using multi-lingual embeddings and recurrent neural networks}

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\titlerunning{ICD-10 coding using multi-lingual embeddings and RNNs}

\author{Jurica \v{S}eva\inst{1} \and
Mario Sänger\inst{1} \and
Ulf Leser\inst{1}}

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\authorrunning{\v{S}eva et al.}

\institute{Humboldt-Universität zu Berlin, Knowledge Management in
Bioinformatics, \\ Berlin, Germany\\
\email{seva,saengema,leser@informatik.hu-berlin.de}}
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\begin{abstract}
This paper describes the participation of the WBI team in the CLEF eHealth 2018 shared task 1 (``Multilingual Information Extraction - ICD-10 coding'').
Our approach builds on two recurrent neural networks models to extract and classify causes of death from French, Italian and Hungarian death certificates.
First, we employ a LSTM-based sequence-to-sequence model to obtain a symptom name from each death certificate line.
We then utilize a bidirectional LSTM model with attention mechanism to assign the respective ICD-10 codes to the received symptom names.
Our model achieves \ldots

\keywords{ICD-10 coding \and Biomedical information extraction \and Multi-lingual sequence-to-sequence model \and Represention learning \and Attention mechanism}
  
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\section{Introduction} 
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\section{Related work}
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\section{Methods}
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\section{Experiments and Results} 
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\section{Conclusion and Future Work}
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