Real-time processing of social media with SENTINEL: a syndromic surveillance system incorporating deep learning for health classification

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Title: Real-time processing of social media with SENTINEL: a syndromic surveillance system incorporating deep learning for health classification
Authors: Hankin, CL
Serban, O
Thapen, N
Maginnis, B
Foot, V
Item Type: Journal Article
Abstract: Interest in real-time syndromic surveillance based on social media data has greatly increased in recent years. The ability to detect disease outbreaks earlier than traditional methods would be highly useful for public health officials. This paper describes a software system which is built upon recent developments in machine learning and data processing to achieve this goal. The system is built from reusable modules integrated into data processing pipelines that are easily deployable and configurable. It applies deep learning to the problem of classifying health-related tweets and is able to do so with high accuracy. It has the capability to detect illness outbreaks from Twitter data and then to build up and display information about these outbreaks, including relevant news articles, to provide situational awareness. It also provides nowcasting functionality of current disease levels from previous clinical data combined with Twitter data. The preliminary results are promising, with the system being able to detect outbreaks of influenza-like illness symptoms which could then be confirmed by existing official sources. The Nowcasting module shows that using social media data can improve prediction for multiple diseases over simply using traditional data sources.
Issue Date: 1-Jun-2018
Date of Acceptance: 28-Apr-2018
URI: http://hdl.handle.net/10044/1/58829
DOI: https://dx.doi.org/10.1016/j.ipm.2018.04.011
ISSN: 0306-4573
Publisher: Elsevier
Journal / Book Title: Information Processing and Management
Copyright Statement: © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/
Sponsor/Funder: Defence Science and Technology Laboratory (DSTL)
Funder's Grant Number: DSTL/AGR/00728/01
Keywords: 0806 Information Systems
0807 Library And Information Studies
Information & Library Sciences
Publication Status: Published online
Online Publication Date: 2018-06-01
Appears in Collections:Faculty of Engineering
Computing



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