Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Impact of data-splits on generalization: Identifying COVID-19 from cough
  and context   Rapidly scaling screening, testing and quarantine has shown to be an
effective strategy to combat the COVID-19 pandemic. We consider the application
of deep learning techniques to distinguish individuals with COVID from
non-COVID by using data acquirable from a phone. Using cough and context
(symptoms and meta-data) represent such a promising approach. Several
independent works in this direction have shown promising results. However, none
of them report performance across clinically relevant data splits.
Specifically, the performance where the development and test sets are split in
time (retrospective validation) and across sites (broad validation). Although
there is meaningful generalization across these splits the performance
significantly varies (up to 0.1 AUC score). In addition, we study the
performance of symptomatic and asymptomatic individuals across these three
splits. Finally, we show that our model focuses on meaningful features of the
input, cough bouts for cough and relevant symptoms for context. The code and
checkpoints are available at https://github.com/WadhwaniAI/cough-against-covid

2
The categories of this paper are: math
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph