Detection of Cases of Noncompliance to Drug Treatment in Patient Forum Posts: Topic Model Approach.
Detection of Cases of Noncompliance to Drug Treatment in Patient Forum Posts: Topic Model Approach.
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DOI：
10.2196/jmir.9222
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发表时间：
2018-03-14
影响因子：
7.4
通讯作者：
Schück S
中科院分区：
文献类型：
--
作者：
Abdellaoui R;Foulquié P;Texier N;Faviez C;Burgun A;Schück S
药物不依从性是许多健康状况管理的主要障碍。更好地了解不依从治疗的潜在因素可能有助于卫生专业人员解决这个问题。患者使用点对点虚拟社区和社交媒体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。本研究的目的是检测描述患者与感兴趣的药物相关的不依从行为的消息。因此，目标是聚集具有与不遵守态度相关的同质词汇的帖子。我们重点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，以识别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消息语料库中发生的主题。数据是使用Kappa Santé设计的网络爬虫收集的，作为Detect项目的一部分，以分析社交媒体的药物安全性。有几个主题与不依从治疗有关。从3650篇与抗抑郁药（艾司西酞普兰）相关的帖子和2164篇与抗精神病药（阿立哌唑）相关的帖子的语料库开始，使用潜在狄利克雷分配使我们能够模拟几个主题，包括治疗中断和剂量变化。主题模型方法检测到的违规行为的召回率为98.5%（272/276），准确率为32.6%（272/844）。主题模型使我们能够探索患者在社区网站上的讨论，并识别与不合规行为相关的帖子。在对不依从主题中的消息进行手动审查后，我们发现6.17%（276/4469）的帖子存在不依从治疗。
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. 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. 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 Santé as part of the Detec’t project to analyze social media for drug safety. Several topics were related to noncompliance to treatment. 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). 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.
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影响因子：
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通讯作者：
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