TY - JOUR AU - Asghari, Mohsen AU - Nielsen, Joshua AU - Gentili, Monica AU - Koizumi, Naoru AU - Elmaghraby, Adel PY - 2022 DA - 2022/11/8 TI -社交媒体上有关活体肾脏捐赠的分类评论:机器学习训练和验证研究乔-地中海JMIR通知SP - e37884六世- 10 - 11 KW -活体肾脏捐赠千瓦肾脏捐赠KW -肾移植KW -文本挖掘KW - web抓取KW - NLP KW -深度学习KW -神经网络KW -肾脏捐赠千瓦壁垒壁垒KW -意识KW -感知KW -机器学习KW -在线源KW -网上评论AB -背景:活体肾脏捐献目前约占所有肾脏捐献的四分之一。存在着阻碍潜在捐献者捐献的障碍，例如医疗条件不合格和与捐献有关的费用。更好地了解人们对活体肾脏捐献的看法和障碍，有助于制定有效的政策、教育机会和推广策略，并可能导致活体肾脏捐献数量的增加。先前的研究主要集中在先前接触过捐赠过程的一小部分人的看法和障碍。在之前的研究中，公众的观点很少被代表。目的:目前的研究设计了一种网络抓取方法和机器学习算法，用于收集和分类来自各种在线资源的评论。由此产生的数据集在公共领域提供，以促进对这一主题的进一步调查。方法:我们使用基于python的网络抓取工具从纽约时报、YouTube、Twitter和Reddit收集评论。 We developed a set of guidelines for the creation of training data and manual classification of comments as either related to living organ donation or not. We then classified the remaining comments using deep learning. Results: A total of 203,219 unique comments were collected from the above sources. The deep neural network model had 84% accuracy in testing data. Further validation of predictions found an actual accuracy of 63%. The final database contained 11,027 comments classified as being related to living kidney donation. Conclusions: The current study lays the groundwork for more comprehensive analyses of perceptions, myths, and feelings about living kidney donation. Web-scraping and machine learning classifiers are effective methods to collect and examine opinions held by the general public on living kidney donation. SN - 2291-9694 UR - https://medinform.www.mybigtv.com/2022/11/e37884 UR - https://doi.org/10.2196/37884 UR - http://www.ncbi.nlm.nih.gov/pubmed/36346661 DO - 10.2196/37884 ID - info:doi/10.2196/37884 ER -