tetano
Editor, Senior Moderator
PLoS One. 2013 Dec 4;8(12):e82489. doi: 10.1371/journal.pone.0082489.
Influenza-Like Illness Surveillance on Twitter through Automated Learning of Na?ve Language.
Gesualdo F, Stilo G, Agricola E, Gonfiantini MV, Pandolfi E, Velardi P, Tozzi AE.
Source
Multifactorial Diseases and Complex Phenotypes Research Area, Bambino Ges? Children's Hospital IRCCS, Rome, Italy.
Abstract
Twitter has the potential to be a timely and cost-effective source of data for syndromic surveillance. When speaking of an illness, Twitter users often report a combination of symptoms, rather than a suspected or final diagnosis, using na?ve, everyday language. We developed a minimally trained algorithm that exploits the abundance of health-related web pages to identify all jargon expressions related to a specific technical term. We then translated an influenza case definition into a Boolean query, each symptom being described by a technical term and all related jargon expressions, as identified by the algorithm. Subsequently, we monitored all tweets that reported a combination of symptoms satisfying the case definition query. In order to geolocalize messages, we defined 3 localization strategies based on codes associated with each tweet. We found a high correlation coefficient between the trend of our influenza-positive tweets and ILI trends identified by US traditional surveillance systems.
PMID:
24324799
[PubMed - in process]
http://www.ncbi.nlm.nih.gov/pubmed/24324799
Influenza-Like Illness Surveillance on Twitter through Automated Learning of Na?ve Language.
Gesualdo F, Stilo G, Agricola E, Gonfiantini MV, Pandolfi E, Velardi P, Tozzi AE.
Source
Multifactorial Diseases and Complex Phenotypes Research Area, Bambino Ges? Children's Hospital IRCCS, Rome, Italy.
Abstract
Twitter has the potential to be a timely and cost-effective source of data for syndromic surveillance. When speaking of an illness, Twitter users often report a combination of symptoms, rather than a suspected or final diagnosis, using na?ve, everyday language. We developed a minimally trained algorithm that exploits the abundance of health-related web pages to identify all jargon expressions related to a specific technical term. We then translated an influenza case definition into a Boolean query, each symptom being described by a technical term and all related jargon expressions, as identified by the algorithm. Subsequently, we monitored all tweets that reported a combination of symptoms satisfying the case definition query. In order to geolocalize messages, we defined 3 localization strategies based on codes associated with each tweet. We found a high correlation coefficient between the trend of our influenza-positive tweets and ILI trends identified by US traditional surveillance systems.
PMID:
24324799
[PubMed - in process]
http://www.ncbi.nlm.nih.gov/pubmed/24324799