Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Encoder-Decoder Model for ICD-10 Coding of Death Certificates   Information extraction from textual documents such as hospital records and
healthrelated user discussions has become a topic of intense interest. The task
of medical concept coding is to map a variable length text to medical concepts
and corresponding classification codes in some external system or ontology. In
this work, we utilize recurrent neural networks to automatically assign ICD-10
codes to fragments of death certificates written in English. We develop
end-to-end neural architectures directly tailored to the task, including basic
encoder-decoder architecture for statistical translation. In order to
incorporate prior knowledge, we concatenate cosine similarities vector among
the text and dictionary entry to the encoded state. Being applied to a standard
benchmark from CLEF eHealth 2017 challenge, our model achieved F-measure of
85.01% on a full test set with significant improvement as compared to the
average score of 62.2% for all official participants approaches.

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The categories of this paper are: nucl-ex
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The categories of this paper are: quant-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-ex
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The categories of this paper are: q-fin
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The categories of this paper are: plasm-ph
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The categories of this paper are: math-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: funct-an
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The categories of this paper are: adap-org
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The categories of this paper are: eess
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The categories of this paper are: physics
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The categories of this paper are: bayes-an
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-th
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The categories of this paper are: q-alg