Filtering big data from social media – Building an early warning system for adverse drug reactions

Yang, Ming; Kiang, Melody Y.; Shang, Wei

doi:10.1016/j.jbi.2015.01.011

Cited by 139 publications

(82 citation statements)

References 33 publications

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“…Patient forums have been also explored using LDA. Yang et al [8] analyzed 1 500 messages from patient forums, to detect adverse drug reactions. The distributions of the themes obtained by applying the LDA model to this corpus made it possible to use similarity measurements for the annotated corpus compared with new messages.…”

Section: Prior Workmentioning

confidence: 99%

“…The original version of LDA modeling proposed by Blei et al [22] has been widely used, e.g., [8,17,19,21,23]. Paul and Dredze developed extensions of the LDA model [18,24,25].…”

Section: Prior Workmentioning

confidence: 99%

“…Three topics described the effects thereof (topics 3,4,8) and one related to its duration (topic 9). Two topics focused on the patients' relationships with healthcare professionals (topic 5) and other individuals (topic 6).…”

Section: Aripiprazole Topicsmentioning

confidence: 99%

“…The example of benfluorex [7] illustrated how social media could be valuable sources for experts. Methods to identify messages related to adverse events have been developed , e.g., [8].…”

Section: Introduction Backgroundmentioning

confidence: 99%

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Detection of Cases of Noncompliance to Drug Treatment in Patient Forum Posts: Topic Model Approach (Preprint)

Abdellaoui¹,

Foulquié²,

Texier³

et al. 2017

Preprint

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Background: Medication non-adherence is a major impediment to the management of many health conditions. A better understanding of the factors underlying non compliance to treatment may help health professionals to address it. Patients use social media to share their experiences regarding their treatments and their diseases. Using topic models makes it possible to model themes present in a collection of posts, thus to identify cases of non-compliance. Objective: Our study aims to detect messages describing patients' non-compliant behaviors. They are associated with a drug of interest. Thus, our objective is the clustering of posts featuring a homogeneous vocabulary related to non-adherent attitudes. Methods: We implemented a probabilistic topic model in order to identify the topics that occurred in a corpus of online messages posted between 2004 and 2013 on three of the most popular French forums. Data were collected using a Web Crawler designed by Kappa Santé as part of the Detec't project to analyze social media for drug safety. Several topics were related to non-compliance to treatment. Results: Starting from a corpus of 3 650 posts related to an antidepressant drug, (escitalopram), and 2,164 posts related to an antipsychotic drug (aripiprazole), the use of latent Dirichlet allocation allowed us to model several themes including interruptions of treatment and changes in dosage. The topic model approach detected cases of non compliance behaviors with a recall of 98.5% and a precision of 32.6%. Conclusions: Topic models enabled us to explore patients' discussions on community websites and to identify posts related with non-compliant behaviors. After a manual review of the messages in the non-compliance topics, we found that non-compliance to treatment was present in almost 6% of the posts.

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Section: Prior Workmentioning

confidence: 99%

“…The original version of LDA modeling proposed by Blei et al [22] has been widely used, e.g., [8,17,19,21,23]. Paul and Dredze developed extensions of the LDA model [18,24,25].…”

Section: Prior Workmentioning

confidence: 99%

Section: Aripiprazole Topicsmentioning

confidence: 99%

“…The example of benfluorex [7] illustrated how social media could be valuable sources for experts. Methods to identify messages related to adverse events have been developed , e.g., [8].…”

Section: Introduction Backgroundmentioning

confidence: 99%

See 2 more Smart Citations

Detection of Cases of Noncompliance to Drug Treatment in Patient Forum Posts: Topic Model Approach (Preprint)

Abdellaoui¹,

Foulquié²,

Texier³

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…Large collections of electronic health records linked to claims data may be screened with natural language processing permitting drug safety reporting outside spontaneous reporting systems, a capability with the promise to merge the strengths of spontaneous reporting with those of insurer databases [5]. Further, novel data sources such as social media can also be mined for adverse events related to drugs [6].…”

mentioning

confidence: 99%