Canfield K
Department of Information Systems, University of Maryland, Baltimore 21228, USA.
Int J Biomed Comput. 1995 May;39(2):263-73. doi: 10.1016/0020-7101(95)01108-q.
This paper describes a methodology for using the knowledge in existing health care text corpora to prime intelligent split menus for provider data-entry. A split menu is one where the top portion of a menu list is organized by user-selection frequency and the bottom portion of the list is traditionally organized in alphabetical order. A simulation shows that data-entry with these intelligent split menus requires between two and five times less effort (in terms of user selections or mouse clicks) than menus arranged alphabetically. This paper uses a corpus from echocardiography to develop the simulation. The methodology uses statistical associations between word categories in the corpus such as 'anatomy' or 'pathology' to prime the frequency ordering of the menus. A dictionary of terms contains the categorical information. After the initial priming, actual user selections are used to update the frequencies used to adapt to providers' individual data-entry patterns.
本文描述了一种利用现有医疗文本语料库中的知识来优化智能拆分菜单以进行提供者数据录入的方法。拆分菜单是指菜单列表的顶部部分按用户选择频率进行组织，而列表的底部部分传统上按字母顺序组织。一项模拟表明，使用这些智能拆分菜单进行数据录入（就用户选择或鼠标点击而言）比按字母顺序排列的菜单所需的工作量少两到五倍。本文使用来自超声心动图的语料库来进行模拟。该方法利用语料库中诸如“解剖学”或“病理学”等词类之间的统计关联来优化菜单的频率排序。术语词典包含分类信息。在初始优化之后，实际用户选择用于更新频率，以适应提供者的个人数据录入模式。
