Exploring Spanish Health Social Media for detecting drug effects

Segura-Bedmar, Isabel; Martínez Fernández, Paloma; Revert Arenaz, Ricardo; Moreno Schneider, Julian

Publication:
Exploring Spanish Health Social Media for detecting drug effects

dc.affiliation.dpto	UC3M. Departamento de Informática	es
dc.affiliation.grupoinv	UC3M. Grupo de Investigación: Human Language and Accessibility Technologies (HULAT)	es
dc.contributor.author	Segura-Bedmar, Isabel
dc.contributor.author	Martínez Fernández, Paloma
dc.contributor.author	Revert Arenaz, Ricardo
dc.contributor.author	Moreno Schneider, Julian
dc.contributor.funder	European Commission	es
dc.date.accessioned	2021-02-03T12:35:54Z
dc.date.available	2021-02-03T12:35:54Z
dc.date.issued	2015-06-15
dc.description.abstract	Background: Adverse Drug reactions (ADR) cause a high number of deaths among hospitalized patients in developed countries. Major drug agencies have devoted a great interest in the early detection of ADRs due to their high incidence and increasing health care costs. Reporting systems are available in order for both healthcare professionals and patients to alert about possible ADRs. However, several studies have shown that these adverse events are underestimated. Our hypothesis is that health social networks could be a significant information source for the early detection of ADRs as well as of new drug indications. Methods: In this work we present a system for detecting drug effects (which include both adverse drug reactions as well as drug indications) from user posts extracted from a Spanish health forum. Texts were processed using MeaningCloud, a multilingual text analysis engine, to identify drugs and effects. In addition, we developed the first Spanish database storing drugs as well as their effects automatically built from drug package inserts gathered from online websites. We then applied a distant-supervision method using the database on a collection of 84,000 messages in order to extract the relations between drugs and their effects. To classify the relation instances, we used a kernel method based only on shallow linguistic information of the sentences. Results: Regarding Relation Extraction of drugs and their effects, the distant supervision approach achieved a recall of 0.59 and a precision of 0.48. Conclusions: The task of extracting relations between drugs and their effects from social media is a complex challenge due to the characteristics of social media texts. These texts, typically posts or tweets, usually contain many grammatical errors and spelling mistakes. Moreover, patients use lay terminology to refer to diseases, symptoms and indications that is not usually included in lexical resources in languages other than English.	en
dc.description.sponsorship	This work was supported by TrendMiner project [FP7-ICT287863] and by MULTIMEDICA project [TIN2010-20644-C03-01].	es
dc.identifier.bibliographicCitation	Segura-Bedmar, I., Martínez, P., Revert, R. et al. Exploring Spanish health social media for detecting drug effects. BMC Med Inform Decis Mak 15, S6 (2015). https://doi.org/10.1186/1472-6947-15-S2-S6	es
dc.identifier.doi	https://doi.org/10.1186/1472-6947-15-S2-S6
dc.identifier.issn	1472-6947
dc.identifier.publicationtitle	BMC Medical Informatics and Decision Making	en
dc.identifier.publicationvolume	15
dc.identifier.uri	https://hdl.handle.net/10016/31853
dc.identifier.uxxi	AR/0000016859
dc.language.iso	eng	es
dc.publisher	Springer Nature	es
dc.relation.projectID	Gobierno de España. TIN2010-20644-C03-01	es
dc.relation.projectID	info:eu-repo/grantAgreement/EC/FP7/ICT287863	es
dc.rights	Copyright © 2015, Segura-Bedmar et al.; licensee BioMed Central Ltd.	es
dc.rights	Atribución 3.0 España	*
dc.rights.accessRights	open access	es
dc.rights.uri	http://creativecommons.org/licenses/by/3.0/es/	*
dc.subject.eciencia	Informática	es
dc.subject.other	Name entity recognition	en
dc.subject.other	Relation extraction	en
dc.subject.other	Annotate Corpus	en
dc.subject.other	Drug indication	en
dc.subject.other	Relation instance	en
dc.title	Exploring Spanish Health Social Media for detecting drug effects	en
dc.type	research article	*
dc.type.hasVersion	VoR	*
dspace.entity.type	Publication