• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of medical Internet research >Identification of Primary Medication Concerns Regarding Thyroid Hormone Replacement Therapy From Online Patient Medication Reviews: Text Mining of Social Network Data
【24h】

Identification of Primary Medication Concerns Regarding Thyroid Hormone Replacement Therapy From Online Patient Medication Reviews: Text Mining of Social Network Data

机译：从在线患者用药评论中确定有关甲状腺激素替代疗法的主要用药问题：社交网络数据的文本挖掘

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

BackgroundPatients with hypothyroidism report poor health-related quality of life despite having undergone thyroid hormone replacement therapy (THRT). Understanding patient concerns regarding levothyroxine can help improve the treatment outcomes of THRT.ObjectiveThis study aimed to (1) identify the distinctive themes in patient concerns regarding THRT, (2) determine whether patients have unique primary medication concerns specific to their demographics, and (3) determine the predictability of primary medication concerns on patient treatment satisfaction.MethodsWe collected patient reviews from WebMD in the United States (1037 reviews about generic levothyroxine and 1075 reviews about the brand version) posted between September 1, 2007, and January 30, 2017. We used natural language processing to identify the themes of medication concerns. Multiple regression analyses were conducted in order to examine the predictability of the primary medication concerns on patient treatment satisfaction.ResultsNatural language processing of the patient reviews of levothyroxine posted on a social networking site produced 6 distinctive themes of patient medication concerns related to levothyroxine treatment: how to take the drug, treatment initiation, dose adjustment, symptoms of pain, generic substitutability, and appearance. Patients had different primary medication concerns unique to their gender, age, and treatment duration. Furthermore, treatment satisfaction on levothyroxine depended on what primary medication concerns the patient had.ConclusionsNatural language processing of text content available on social media could identify different themes of patient medication concerns that can be validated in future studies to inform the design of tailored medication counseling for improved patient treatment satisfaction.
机译：背景甲状腺功能减退症患者尽管接受了甲状腺激素替代疗法（THRT），但仍报告与健康相关的不良生活质量。理解患者对左甲状腺素的担忧可以帮助改善THRT的治疗效果。目的本研究旨在（1）识别患者对THRT的关注主题，（2）确定患者是否具有针对其人口统计学的独特主要药物关注，以及（3）方法）我们从美国WebMD收集了2007年9月1日至2017年1月30日期间发布的患者评论（关于通用左旋甲状腺素的1037条评论和有关品牌版本的1075条评论）。我们使用自然语言处理来确定药物问题的主题。进行了多元回归分析，以检验主要药物关注对患者治疗满意度的可预测性。结果在社交网站上发布的左旋甲状腺素患者评论的自然语言处理产生了六个与左旋甲状腺素治疗相关的患者药物关注的主题：如何服用药物，治疗开始，剂量调整，疼痛症状，通用替代性和外观。患者的性别，年龄和治疗时间长短不同，对主要药物的关注也不同。此外，对左甲状腺素的治疗满意度取决于患者所使用的主要药物。结论自然语言对社交媒体上可用文本内容的处理可以识别出患者药物关注的不同主题，这些主题可以在未来的研究中得到验证，从而为针对性药物咨询设计提供依据提高患者的治疗满意度。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号