TY -的AU -抽梗机,玛雅AU - Parmet,以色列非盟- Ravid Gilad PY - 2022 DA - 2022/8/2 TI -识别患者炎症性肠病在Twitter和学习从他们的个人经验:回顾性队列研究乔- J地中海互联网Res SP - e29186六世- 24 - 8 KW -病人识别KW -炎症性肠病KW - IBD KW -用户分类KW - Twitter KW -自然语言处理KW - NLP KW -情绪分析AB -背景:患者使用社交媒体作为另一种信息来源，在那里他们分享信息并提供社会支持。尽管Twitter和其他社交网络平台每天都会发布大量与健康相关的数据，但利用社交媒体数据来了解慢性病和患者生活方式的研究是有限的。目的:在这项研究中，我们通过提供一个在Twitter上识别炎症性肠病(IBD)患者并从他们的个人经历中学习的框架，为缩小这一差距做出了贡献。我们通过构建一个区分患者和其他实体的Twitter用户分类器来分析患者的推文。这项研究旨在揭示利用Twitter数据促进IBD患者福祉的潜力，依靠人群的智慧来确定健康的生活方式。我们试图利用描述患者日常活动及其对健康的影响的帖子来描述与生活方式相关的治疗。方法:在研究的第一阶段，采用社会网络分析和自然语言处理相结合的机器学习方法，对用户进行患者和非患者自动分类。我们考虑了3种类型的特征:用户在Twitter上的行为，用户tweets的内容，以及用户网络的社会结构。我们比较了两种分类方法中几种分类算法的性能。 One classified each tweet and deduced the user’s class from their tweet-level classification. The other aggregated tweet-level features to user-level features and classified the users themselves. Different classification algorithms were examined and compared using 4 measures: precision, recall, F1 score, and the area under the receiver operating characteristic curve. In the second stage, a classifier from the first stage was used to collect patients' tweets describing the different lifestyles patients adopt to deal with their disease. Using IBM Watson Service for entity sentiment analysis, we calculated the average sentiment of 420 lifestyle-related words that patients with IBD use when describing their daily routine. Results: Both classification approaches showed promising results. Although the precision rates were slightly higher for the tweet-level approach, the recall and area under the receiver operating characteristic curve of the user-level approach were significantly better. Sentiment analysis of tweets written by patients with IBD identified frequently mentioned lifestyles and their influence on patients’ well-being. The findings reinforced what is known about suitable nutrition for IBD as several foods known to cause inflammation were pointed out in negative sentiment, whereas relaxing activities and anti-inflammatory foods surfaced in a positive context. Conclusions: This study suggests a pipeline for identifying patients with IBD on Twitter and collecting their tweets to analyze the experimental knowledge they share. These methods can be adapted to other diseases and enhance medical research on chronic conditions. SN - 1438-8871 UR - //www.mybigtv.com/2022/8/e29186 UR - https://doi.org/10.2196/29186 UR - http://www.ncbi.nlm.nih.gov/pubmed/35917151 DO - 10.2196/29186 ID - info:doi/10.2196/29186 ER -