TY -非盟的荣格Yuchul盟,户珥Cinyoung盟——荣格,Dain AU -金,Minki PY - 2015 DA - 2015/04/07 TI -识别关键在线健康社区医院服务质量因素乔- J地中海互联网Res SP - e90六世- 17 - 4 KW -医院服务因素KW -在线健康社区医院千瓦KW -社会媒体关键质量因素——推荐类型分类KW -质量因素分析KW -医疗保健政策AB -背景:与健康相关的用户创建内容的数量，特别是在线健康社区中与医院相关的问题和答案，迅速增加。患者和护理人员参与在线社区活动，分享他们的经验，交换信息，并询问被推荐或不可信的医院。然而，如何从网络社区自动识别医院服务质量的研究很少。过去，对医院的深入分析采用随机抽样调查。然而，由于在线数据量的快速增长和相关利益相关者的不同分析需求，这种调查正在变得不切实际。目的:作为一种利用大规模健康相关信息的解决方案，我们提出了一种从在线健康社区中自动识别医院服务质量因素和加班趋势的新方法，特别是与医院相关的问题和答案。方法:定义基于社交媒体的医院质量关键因素。此外，我们开发了文本挖掘技术来检测在线健康社区中经常出现的这些因素。在检测到这些代表医院定性方面的因素后，我们应用情感分析来识别在线健康社区中发布的消息中的建议类型。 Korea’s two biggest online portals were used to test the effectiveness of detection of social media–based key quality factors for hospitals. Results: To evaluate the proposed text mining techniques, we performed manual evaluations on the extraction and classification results, such as hospital name, service quality factors, and recommendation types using a random sample of messages (ie, 5.44% (9450/173,748) of the total messages). Service quality factor detection and hospital name extraction achieved average F1 scores of 91% and 78%, respectively. In terms of recommendation classification, performance (ie, precision) is 78% on average. Extraction and classification performance still has room for improvement, but the extraction results are applicable to more detailed analysis. Further analysis of the extracted information reveals that there are differences in the details of social media–based key quality factors for hospitals according to the regions in Korea, and the patterns of change seem to accurately reflect social events (eg, influenza epidemics). Conclusions: These findings could be used to provide timely information to caregivers, hospital officials, and medical officials for health care policies. SN - 1438-8871 UR - //www.mybigtv.com/2015/4/e90/ UR - https://doi.org/10.2196/jmir.3646 UR - http://www.ncbi.nlm.nih.gov/pubmed/25855612 DO - 10.2196/jmir.3646 ID - info:doi/10.2196/jmir.3646 ER -