Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Automatic detection of surgical site infections from a clinical data
  warehouse   Reducing the incidence of surgical site infections (SSIs) is one of the
objectives of the French nosocomial infection control program. Manual
monitoring of SSIs is carried out each year by the hospital hygiene team and
surgeons at the University Hospital of Bordeaux. Our goal was to develop an
automatic detection algorithm based on hospital information system data. Three
years (2015, 2016 and 2017) of manual spine surgery monitoring have been used
as a gold standard to extract features and train machine learning algorithms.
The dataset contained 22 SSIs out of 2133 spine surgeries. Two different
approaches were compared. The first used several data sources and achieved the
best performance but is difficult to generalize to other institutions. The
second was based on free text only with semiautomatic extraction of
discriminant terms. The algorithms managed to identify all the SSIs with 20 and
26 false positives respectively on the dataset. Another evaluation is underway.
These results are encouraging for the development of semi-automated
surveillance methods.

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The categories of this paper are: atom-ph
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
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The categories of this paper are: acc-phys
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The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph