Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Patient Risk Assessment and Warning Symptom Detection Using Deep
  Attention-Based Neural Networks   We present an operational component of a real-world patient triage system.
Given a specific patient presentation, the system is able to assess the level
of medical urgency and issue the most appropriate recommendation in terms of
best point of care and time to treat. We use an attention-based convolutional
neural network architecture trained on 600,000 doctor notes in German. We
compare two approaches, one that uses the full text of the medical notes and
one that uses only a selected list of medical entities extracted from the text.
These approaches achieve 79% and 66% precision, respectively, but on a
confidence threshold of 0.6, precision increases to 85% and 75%, respectively.
In addition, a method to detect warning symptoms is implemented to render the
classification task transparent from a medical perspective. The method is based
on the learning of attention scores and a method of automatic validation using
the same data.

23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex