Detection and analysis of drug non-compliance ...
Document type :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Title :
Detection and analysis of drug non-compliance in internet fora using information retrieval approaches
Author(s) :
Bigeard, Lise [Auteur]
Savoirs, Textes, Langage (STL) - UMR 8163 [STL]
Thiessard, Frantz [Auteur]
CHU Bordeaux [Bordeaux]
Grabar, Natalia [Auteur]
Savoirs, Textes, Langage (STL) - UMR 8163 [STL]
Savoirs, Textes, Langage (STL) - UMR 8163 [STL]
Thiessard, Frantz [Auteur]
CHU Bordeaux [Bordeaux]
Grabar, Natalia [Auteur]

Savoirs, Textes, Langage (STL) - UMR 8163 [STL]
Conference title :
CICLING 2019
City :
La Rochelle
Country :
France
Start date of the conference :
2019-04-07
HAL domain(s) :
Informatique [cs]
Sciences du Vivant [q-bio]
Sciences du Vivant [q-bio]
English abstract : [en]
In the health-related field, drug non-compliance situations happen when patients do not follow their prescriptions and do actions which lead to potentially harmful situations. Although such situations are dangerous, patients ...
Show more >In the health-related field, drug non-compliance situations happen when patients do not follow their prescriptions and do actions which lead to potentially harmful situations. Although such situations are dangerous, patients usually do not report them to their physicians. Hence, it is necessary to study other sources of information. We propose to study online health fora with information retrieval methods in order to identify messages that contain drug non-compliance information. Exploitation of information retrieval methods permits to detect non-compliance messages with up to 0.529 F-measure, compared to 0.824 F-measure reached with supervized machine learning methods. For some fine-grained categories and on new data, it shows up to 0.70 Precision.Show less >
Show more >In the health-related field, drug non-compliance situations happen when patients do not follow their prescriptions and do actions which lead to potentially harmful situations. Although such situations are dangerous, patients usually do not report them to their physicians. Hence, it is necessary to study other sources of information. We propose to study online health fora with information retrieval methods in order to identify messages that contain drug non-compliance information. Exploitation of information retrieval methods permits to detect non-compliance messages with up to 0.529 F-measure, compared to 0.824 F-measure reached with supervized machine learning methods. For some fine-grained categories and on new data, it shows up to 0.70 Precision.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
ANR Project :
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