Luo Gang
IBM T.J. Watson Research Center, 19 Skyline Drive, Hawthorne, NY 10532, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:2167-71. doi: 10.1109/IEMBS.2010.5626435.
To help people find desired home medical products (HMPs), we developed an intelligent personal health record (iPHR) system that can automatically recommend HMPs based on users' health issues. Using nursing knowledge, we pre-compile a set of "search guide" phrases that provides semantic translation from words describing health issues to their underlying medical meanings. Then iPHR automatically generates queries from those phrases and uses them and a search engine to retrieve HMPs. To avoid missing relevant HMPs during retrieval, the compiled search guide phrases need to be comprehensive. Such compilation is a challenging task because nursing knowledge updates frequently and contains numerous details scattered in many sources. This paper presents a semi-automatic tool facilitating such compilation. Our idea is to formulate the phrase compilation task as a multi-label classification problem. For each newly obtained search guide phrase, we first use nursing knowledge and information retrieval techniques to identify a small set of potentially relevant classes with corresponding hints. Then a nurse makes the final decision on assigning this phrase to proper classes based on those hints. We demonstrate the effectiveness of our techniques by compiling search guide phrases from an occupational therapy textbook.
为了帮助人们找到所需的家用医疗产品(HMP),我们开发了一种智能个人健康记录(iPHR)系统,该系统可以根据用户的健康问题自动推荐HMP。利用护理知识,我们预先编译了一组“搜索指南”短语,这些短语提供了从描述健康问题的词汇到其潜在医学含义的语义翻译。然后,iPHR会根据这些短语自动生成查询,并使用它们和搜索引擎来检索HMP。为了避免在检索过程中遗漏相关的HMP,编译的搜索指南短语需要全面。这样的编译是一项具有挑战性的任务,因为护理知识经常更新,且包含分散在许多来源中的大量细节。本文提出了一种便于此类编译的半自动工具。我们的想法是将短语编译任务表述为一个多标签分类问题。对于每个新获得的搜索指南短语,我们首先使用护理知识和信息检索技术来识别一小部分具有相应提示的潜在相关类别。然后,护士根据这些提示对将该短语分配到适当的类别做出最终决定。我们通过从一本职业治疗教科书中编译搜索指南短语来证明我们技术的有效性。