Abstract
Analyzing how people discuss about health-related topics on dedicated forums and social networks such as Twitter, can provide valuable insight for syndromic surveillance and to predict disease outbreaks. In this paper we present a minimally trained algorithm to learn associations between technical and everyday language terms, based on pattern generalization and complete linkage clustering, and we then assess its utility on a case study of five common syndromes for surveillance purposes.
| Original language | English |
|---|---|
| Title of host publication | International Conference Recent Advances in Natural Language Processing, RANLP |
| Pages | 640-648 |
| Number of pages | 9 |
| Publication status | Published - 2013 |
| Event | 9th International Conference on Recent Advances in Natural Language Processing, RANLP 2013 - Hissar, Bulgaria Duration: Sep 9 2013 → Sep 11 2013 |
Other
| Other | 9th International Conference on Recent Advances in Natural Language Processing, RANLP 2013 |
|---|---|
| Country | Bulgaria |
| City | Hissar |
| Period | 9/9/13 → 9/11/13 |
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ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Software
- Electrical and Electronic Engineering
Cite this
Automated learning of everyday patients' language for medical blogs analytics. / Stilo, Giovanni; De Vincenzi, Moreno; Tozzi, Alberto E.; Velardi, Paola.
International Conference Recent Advances in Natural Language Processing, RANLP. 2013. p. 640-648.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Automated learning of everyday patients' language for medical blogs analytics
AU - Stilo, Giovanni
AU - De Vincenzi, Moreno
AU - Tozzi, Alberto E.
AU - Velardi, Paola
PY - 2013
Y1 - 2013
N2 - Analyzing how people discuss about health-related topics on dedicated forums and social networks such as Twitter, can provide valuable insight for syndromic surveillance and to predict disease outbreaks. In this paper we present a minimally trained algorithm to learn associations between technical and everyday language terms, based on pattern generalization and complete linkage clustering, and we then assess its utility on a case study of five common syndromes for surveillance purposes.
AB - Analyzing how people discuss about health-related topics on dedicated forums and social networks such as Twitter, can provide valuable insight for syndromic surveillance and to predict disease outbreaks. In this paper we present a minimally trained algorithm to learn associations between technical and everyday language terms, based on pattern generalization and complete linkage clustering, and we then assess its utility on a case study of five common syndromes for surveillance purposes.
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UR - http://www.scopus.com/inward/citedby.url?scp=84890543001&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84890543001
SP - 640
EP - 648
BT - International Conference Recent Advances in Natural Language Processing, RANLP
ER -