Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: EHRKit: A Python Natural Language Processing Toolkit for Electronic
  Health Record Texts   The Electronic Health Record (EHR) is an essential part of the modern medical
system and impacts healthcare delivery, operations, and research. Unstructured
text is attracting much attention despite structured information in the EHRs
and has become an exciting research field. The success of the recent neural
Natural Language Processing (NLP) method has led to a new direction for
processing unstructured clinical notes. In this work, we create a python
library for clinical texts, EHRKit. This library contains two main parts:
MIMIC-III-specific functions and tasks specific functions. The first part
introduces a list of interfaces for accessing MIMIC-III NOTEEVENTS data,
including basic search, information retrieval, and information extraction. The
second part integrates many third-party libraries for up to 12 off-shelf NLP
tasks such as named entity recognition, summarization, machine translation,
etc.

21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
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The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
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The categories of this paper are: mtrl-th
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-alg
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The categories of this paper are: eess
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The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga