%0期刊文章%@ 1438- 8871% I JMIR出版物%V 24卡塔尔世界杯8强波胆分析% N 8% P e29186% T在Twitter上识别炎症性肠病患者并从他们的个人经验中学习:回顾性队列研究%A Stemmer,Maya %A Parmet,Yisrael %A Ravid,Gilad %+内盖夫本-古里安大学工业工程与管理系，POB 653, Beer-Sheva, 84105，以色列，972 8 6461434,mayast@post.bgu.ac.il %K患者鉴定%K炎症性肠病%K IBD %K用户分类%K推特%K自然语言处理%K NLP %K情感分析%D 2022 %7 2.8.2022 %9原始论文%J J医学互联网Res %G英语%X背景:患者使用社交媒体作为替代信息来源，在那里他们分享信息并提供社会支持。尽管每天都有大量与健康相关的数据发布在Twitter和其他社交网络平台上，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，我们通过提供一个框架来在Twitter上识别炎症性肠病(IBD)患者，并从他们的个人经历中学习，从而有助于缩小这一差距。我们通过构建一个Twitter用户分类器来实现对患者推文的分析，该分类器将患者与其他实体区分开来。这项研究旨在揭示利用Twitter数据来促进IBD患者健康的潜力，依靠人群的智慧来识别健康的生活方式。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，使用社会网络分析和自然语言处理相结合的机器学习方法自动将用户分类为患者或非患者。我们考虑了三种类型的特征:用户在Twitter上的行为，用户推文的内容，以及用户网络的社交结构。 We compared the performances of several classification algorithms within 2 classification approaches. 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. %M 35917151 %R 10.2196/29186 %U //www.mybigtv.com/2022/8/e29186 %U https://doi.org/10.2196/29186 %U http://www.ncbi.nlm.nih.gov/pubmed/35917151