Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Clinical Concept Extraction for Document-Level Coding   The text of clinical notes can be a valuable source of patient information
and clinical assessments. Historically, the primary approach for exploiting
clinical notes has been information extraction: linking spans of text to
concepts in a detailed domain ontology. However, recent work has demonstrated
the potential of supervised machine learning to extract document-level codes
directly from the raw text of clinical notes. We propose to bridge the gap
between the two approaches with two novel syntheses: (1) treating extracted
concepts as features, which are used to supplement or replace the text of the
note; (2) treating extracted concepts as labels, which are used to learn a
better representation of the text. Unfortunately, the resulting concepts do not
yield performance gains on the document-level clinical coding task. We explore
possible explanations and future research directions.

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The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
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The categories of this paper are: nucl-th
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The categories of this paper are: adap-org
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The categories of this paper are: gr-qc
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The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
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The categories of this paper are: quant-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: alg-geom
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The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
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The categories of this paper are: math
33
The categories of this paper are: plasm-ph
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The categories of this paper are: nlin