Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Discovering key topics from short, real-world medical inquiries via
  natural language processing and unsupervised learning   Millions of unsolicited medical inquiries are received by pharmaceutical
companies every year. It has been hypothesized that these inquiries represent a
treasure trove of information, potentially giving insight into matters
regarding medicinal products and the associated medical treatments. However,
due to the large volume and specialized nature of the inquiries, it is
difficult to perform timely, recurrent, and comprehensive analyses. Here, we
propose a machine learning approach based on natural language processing and
unsupervised learning to automatically discover key topics in real-world
medical inquiries from customers. This approach does not require ontologies nor
annotations. The discovered topics are meaningful and medically relevant, as
judged by medical information specialists, thus demonstrating that unsolicited
medical inquiries are a source of valuable customer insights. Our work paves
the way for the machine-learning-driven analysis of medical inquiries in the
pharmaceutical industry, which ultimately aims at improving patient care.

23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat