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.
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Transfer learning for unsupervised influenza-like illness models from online search data |
| Event: | WWW '19 The World Wide Web Conference |
| Location: | San Francisco, CA, USA |
| Dates: | 13 - 17 May 2019 |
| ISBN-13: | 978-1-4503-6674-8 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3308558.3313477 |
| Publisher version: | https://doi.org/10.1145/3308558.3313477 |
| Language: | English |
| Additional information: | 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/ |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10074319 |
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