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

被引：32

作者：

Abdellaoui, Redhouane ^{[1
]}

Foulquie, Pierre ^{[2
]}

Texier, Nathalie ^{[2
]}

Faviez, Carole ^{[2
]}

Burgun, Anita ^{[1
,3
]}

Schuck, Stephane ^{[2
]}

机构：

[1] Univ Paris 06, INSERM, Unite Mixte Rech 1138, Team 22, 15 Rue Ecole Med, F-75006 Paris, France

[2] Kappa Sante, Innovat Kap Code, Paris, France

[3] Hop Europeen Georges Pompidou, AP HP, Med Informat, Paris, France

来源：

JOURNAL OF MEDICAL INTERNET RESEARCH | 2018年 / 20卷 / 03期

关键词：

medication adherence; compliance; infodemiology; social media; text mining; depression; psychosis; peer-to-peer support; virtual community; SOCIAL MEDIA; TWEETS; REASONS;

D O I：

10.2196/jmir.9222

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

Background: Medication nonadherence is a major impediment to the management of many health conditions. A better understanding of the factors underlying noncompliance to treatment may help health professionals to address it. Patients use peer-to-peer virtual communities and social media to share their experiences regarding their treatments and diseases. Using topic models makes it possible to model themes present in a collection of posts, thus to identify cases of noncompliance. Objective: The aim of this study was to detect messages describing patients' noncompliant behaviors associated with a drug of interest. Thus, the objective was the clustering of posts featuring a homogeneous vocabulary related to nonadherent attitudes. Methods: We focused on escitalopram and aripiprazole used to treat depression and psychotic conditions, respectively. We implemented a probabilistic topic model to identify the topics that occurred in a corpus of messages mentioning these drugs, posted from 2004 to 2013 on three of the most popular French forums. Data were collected using a Web crawler designed by Kappa Sante as part of the Detec't project to analyze social media for drug safety. Several topics were related to noncompliance to treatment. Results: Starting from a corpus of 3650 posts related to an antidepressant drug (escitalopram) and 2164 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 noncompliance behaviors with a recall of 98.5% (272/276) and a precision of 32.6% (272/844). Conclusions: Topic models enabled us to explore patients' discussions on community websites and to identify posts related with noncompliant behaviors. After a manual review of the messages in the noncompliance topics, we found that noncompliance to treatment was present in 6.17% (276/4469) of the posts.

引用

页数：12

共 42 条

[1]

Abdellaoui Redhouane, 2017, JMIR Public Health Surveill, V3, pe36, DOI 10.2196/publichealth.6577

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