eprintid: 10074319
rev_number: 25
eprint_status: archive
userid: 608
dir: disk0/10/07/43/19
datestamp: 2019-05-22 08:43:54
lastmod: 2021-10-10 22:43:26
status_changed: 2019-05-22 08:43:54
type: proceedings_section
metadata_visibility: show
creators_name: Zou, B
creators_name: Lampos, V
creators_name: Cox, I
title: Transfer learning for unsupervised influenza-like illness models from online search data
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
note: This paper is published under the Creative Commons Attribution 4.0 International
(CC-BY 4.0) license. Authors reserve their rights to disseminate the work on their
personal and corporate Web sites with the appropriate attribution
https://creativecommons.org/licenses/by/4.0/
abstract: A considerable body of research has demonstrated that online
search data can be used to complement current syndromic surveillance systems. The vast majority of previous work proposes solutions that are based on supervised learning paradigms, in which
historical disease rates are required for training a model. However,
for many geographical regions this information is either sparse or
not available due to a poor health infrastructure. It is these regions
that have the most to benefit from inferring population health statistics from online user search activity. To address this issue, we
propose a statistical framework in which we first learn a supervised model for a region with adequate historical disease rates, and
then transfer it to a target region, where no syndromic surveillance
data exists. This transfer learning solution consists of three steps:
(i) learn a regularized regression model for a source country, (ii)
map the source queries to target ones using semantic and temporal similarity metrics, and (iii) re-adjust the weights of the target
queries. It is evaluated on the task of estimating influenza-like illness (ILI) rates. We learn a source model for the United States, and
subsequently transfer it to three other countries, namely France,
Spain and Australia. Overall, the transferred (unsupervised) models
achieve strong performance in terms of Pearson correlation with
the ground truth (> .92 on average), and their mean absolute error
does not deviate greatly from a fully supervised baseline.
date: 2019-05-12
date_type: published
publisher: ACM
official_url: https://doi.org/10.1145/3308558.3313477
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1658259
doi: 10.1145/3308558.3313477
isbn_13: 978-1-4503-6674-8
lyricists_name: Cox, Ingemar
lyricists_name: Lampos, Vasileios
lyricists_id: IJCOX77
lyricists_id: VLAMP72
actors_name: Lampos, Vasileios
actors_id: VLAMP72
actors_role: owner
full_text_status: public
series: The World Wide Web Conference
publication: The World Wide Web Conference
volume: 2019
place_of_pub: San Francisco, CA, USA
pagerange: 2505-2516
event_title: WWW '19 The World Wide Web Conference
event_location: San Francisco, CA, USA
event_dates: 13 - 17 May 2019
book_title: Proceeding WWW '19 The World Wide Web Conference
citation:        Zou, B;    Lampos, V;    Cox, I;      (2019)    Transfer learning for unsupervised influenza-like illness models from online search data.                     In:  Proceeding WWW '19 The World Wide Web Conference.  (pp. pp. 2505-2516).  ACM: San Francisco, CA, USA.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10074319/1/p2505-zou.pdf