TY -的AU -雅司病,文静AU -邱,克里斯托弗·SG AU - Chi林建兴PY - 2017 DA - 2017/12/21 TI -自动分类用户的健康信息需要上下文:鼠标单击和眼动跟踪数据逻辑回归分析乔- J地中海互联网Res SP - e424六世- 19 - 12 KW -信息寻求行为KW -社会媒体KW -互联网KW -消费者健康信息KW -医学信息学AB -背景:在互联网上搜索健康信息的用户可能是在搜索自己的健康问题，也可能是在搜索别人的健康问题，或者是在浏览时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需求上下文，则可以定制提供给用户的搜索结果或信息，以增加其对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(搜索自我、搜索他人或无特定健康问题浏览)的可能性;利用眼球追踪信息;结合使用眼球追踪，人口统计和紧急信息。利用多项逻辑回归建立了预测模型。方法:74名参与者(39名女性，35名男性)主要是一所大学的教职员工和学生，他们被要求浏览健康论坛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. SN - 1438-8871 UR - //www.mybigtv.com/2017/12/e424/ UR - https://doi.org/10.2196/jmir.8354 UR - http://www.ncbi.nlm.nih.gov/pubmed/29269342 DO - 10.2196/jmir.8354 ID - info:doi/10.2196/jmir.8354 ER -