%0期刊文章%@ 1438-8871 %I JMIR出版物%V 19%卡塔尔世界杯8强波胆分析 N 12% P e424 %T用户健康信息自动分类需要上下文:鼠标点击与眼动数据的Logistic回归分析%A Pian，文静%A Khoo,Christopher SG %A Chi，建兴%+福建师范大学传媒学院，福州350117，中国，86 13696889850,islandma@foxmail.com %K信息搜索行为%K社交媒体%K互联网%K消费者健康信息%K医疗信息%D 2017 %7 21.12.2017 %9原创论文%J J医学互联网Res %G英文%X背景:在Internet上搜索健康信息的用户可能是在搜索自己的健康问题、搜索别人的健康问题，或者浏览时并没有考虑到特定的健康问题。之前的研究发现，这三类用户关注不同类型的健康信息。然而，大多数健康信息网站为所有用户提供静态内容。如果Web应用程序可以识别这三种类型的用户健康信息，那么可以定制提供给用户的搜索结果或信息，以增加其与用户的相关性或有用性。目的:本研究的目的是探讨仅使用超链接点击行为识别三种用户健康信息上下文(搜索自己，搜索他人，或浏览没有特定健康问题)的可能性;使用眼球追踪信息;并结合使用眼球追踪、人口统计和紧急信息。使用多项逻辑回归建立预测模型。 Methods: A total of 74 participants (39 females and 35 males) who were mainly staff and students of a university were asked to browse a health discussion forum, Healthboards.com. An eye tracker recorded their examining (eye fixation) and skimming (quick eye movement) behaviors on 2 types of screens: summary result screen displaying a list of post headers, and detailed post screen. The following three types of predictive models were developed using logistic regression analysis: model 1 used only the time spent in scanning the summary result screen and reading the detailed post screen, which can be determined from the user’s mouse clicks; model 2 used the examining and skimming durations on each screen, recorded by an eye tracker; and model 3 added user demographic and urgency information to model 2. Results: An analysis of variance (ANOVA) analysis found that users’ browsing durations were significantly different for the three health information contexts (P<.001). The logistic regression model 3 was able to predict the user’s type of health information context with a 10-fold cross validation mean accuracy of 84% (62/74), followed by model 2 at 73% (54/74) and model 1 at 71% (52/78). In addition, correlation analysis found that particular browsing durations were highly correlated with users’ age, education level, and the urgency of their information need. Conclusions: A user’s type of health information need context (ie, searching for self, for others, or with no health issue in mind) can be identified with reasonable accuracy using just user mouse clicks that can easily be detected by Web applications. Higher accuracy can be obtained using Google glass or future computing devices with eye tracking function. %M 29269342 %R 10.2196/jmir.8354 %U //www.mybigtv.com/2017/12/e424/ %U https://doi.org/10.2196/jmir.8354 %U http://www.ncbi.nlm.nih.gov/pubmed/29269342