%0期刊文章%@ 1438-8871 %I JMIR出版物%V 20%卡塔尔世界杯8强波胆分析 N 3% P e85% T在患者论坛帖子中发现不符合药物治疗的案例:主题模型方法%A Abdellaoui,Redhouane %A Foulquié，Pierre %A Texier,Nathalie %A Faviez,Carole %A Burgun,Anita %A Schück,Stéphane %+ Unité de Mixte de Recherche 1138 22队，国家研究所Santé et de Faviez Médicale / Université Pierre et Marie Curie, 15 Rue del 'École de Médecine，巴黎，75006，法国，33 648094269，redhouane.a@gmail.com %K药物依从性%K依从性%K信息流行病学%K社交媒体%K文本挖掘%K抑郁症%K精神病%K点对点支持%K虚拟社区%D 2018 %7 14.03.2018 %9原始论文%J J医学互联网Res %G英语%X背景:药物不依从性是许多健康状况管理的主要障碍。更好地了解不遵守治疗的潜在因素可能有助于卫生专业人员解决这一问题。患者使用点对点虚拟社区和社交媒体来分享他们关于治疗和疾病的经验。使用主题模型可以对帖子集合中的主题进行建模，从而识别不合规的情况。目的:本研究的目的是检测描述患者与感兴趣的药物相关的不服从行为的信息。因此，目标是聚类具有与非坚持态度相关的同质词汇的帖子。方法:我们关注艾司西酞普兰和阿立哌唑分别用于治疗抑郁症和精神病。我们实施了一个概率主题模型，以确定2004年至2013年在三个最受欢迎的法国论坛上发布的提及这些药物的语料库中出现的主题。 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 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. %M 29540337 %R 10.2196/jmir.9222 %U //www.mybigtv.com/2018/3/e85/ %U https://doi.org/10.2196/jmir.9222 %U http://www.ncbi.nlm.nih.gov/pubmed/29540337