Initial paper version

paper/wbi-clef18.tex

+% This is samplepaper.tex, a sample chapter demonstrating the
+% LLNCS macro package for Springer Computer Science proceedings;
+% Version 2.20 of 2017/10/04
+%
+\documentclass[runningheads]{llncs}
+\usepackage[utf8]{inputenc} 
+\usepackage[english]{babel} 
+\usepackage{color}
+ 
+% Used for displaying a sample figure. If possible, figure files should
+% be included in EPS format.
+\usepackage{graphicx}
+
+% If you use the hyperref package, please uncomment the following line
+% to display URLs in blue roman font according to Springer's eBook style:
+% \renewcommand\UrlFont{\color{blue}\rmfamily}
+
+\begin{document}
+
+\newcommand{\nm}[1]{\textcolor{green}{Mario: #1}\\}
+\newcommand{\nj}[1]{\textcolor{blue}{Jurica: #1}\\}
+
+\title{WBI at CLEF eHealth 2018 Task 1: Language-independent ICD10 coding using 
+multi-lingual embeddings and recurrent neural networks}
+
+
+% If the paper title is too long for the running head, you can set
+% an abbreviated paper title here
+%\titlerunning{Abbreviated paper title}
+
+\author{Jurica Séva\inst{1} \and
+Mario Sänger\inst{1} \and
+Ulf Leser\inst{1}}
+
+% First names are abbreviated in the running head.
+% If there are more than two authors, 'et al.' is used.
+\authorrunning{Séva et al.}
+
+\institute{Humboldt-Universität zu Berlin, Knowledge Management in
+Bioinformatics, \\ Berlin, Germany\\
+\email{seva,saengema,leser@informatik.hu-berlin.de}}
+%
+\maketitle              % typeset the header of the contribution
+% 
+\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
+train a LSTM-based sequence-to-sequence model to obtain a death cause
+descriptions for death certificate lines. Then we utilize a bidirectional LSTM
+model with attention mechanism to assign the ICD-10 code for a given death cause
+description. 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}
+  
+\end{abstract}
+
+\section{Introduction}
+\nm{TODO: Insert text!}
+Automatic extraction, classification and analysis of biological and medical
+concepts from unstructured texts, such as scientific publications or electronic
+health documents, is a highly important task to support many applications in
+research, daily clinical routine and policy-making. Computer-aided approaches
+can improve decision making and support clinical processes, for example, by
+giving a more sophisticated overview about a research area, providing detailed
+information about the aetiopathology of a patient or disease patterns.
+
+The CLEF eHealth lab attends to this circumstance through organization of
+various shared tasks which aid and support the development of approaches to
+exploit electronically available medical content. In particular, Task 1 of the
+lab was concerned with the extraction and classification of causes of death from
+death certificates originating from different languages. Participants were asked
+to classify the death causes mentioned in the certificates according to the
+International Classification of Disease version 10 (ICD-10). The task has been
+carried out the last two years of the lab, however was only concerned
+with French and English certificates. In contrast, the organizers provided
+annotated death reports as well as ICD-10 dictionaries for French, Italian and
+Hungarian this year. The development of language-independent, multilingual approaches
+was encouraged.
+
+\section{Related work}
+\nm{TODO: Insert text!}
+
+\section{Methods}
+\nm{TODO: Insert text!}
+
+\section{Experiments and Results}
+\nj{TODO: Insert text!}
+
+\section{Conclusion and Future Work}
+\nj{TODO: Insert text!}
+
+\end{document}