Department of Information Systems, Informatics Institute, Middle East Technical University, İnönü Buvari,06531 Ankara, Turkey.
Inform Health Soc Care. 2011 Mar;36(2):100-15. doi: 10.3109/17538157.2010.506252. Epub 2010 Dec 8.
A major problem in biomedical informatics is the contextual retrieval and ranking of medical and healthcare information. In this article, we present a model for extracting semantic relations among medical and clinical documents. The purpose is to maximise contextual retrieval and ranking performance with minimum input from users.
We developed and evaluated a medical search engine that relies on a multi-features similarity model. The indexed documents are represented as a network that reflects the semantic relations among documents to assess topical rankings.
The evaluation measurements include the following: recall, precision and R-precision. We used OHSUMED collection to evaluate our work with runs submitted to TREC-9. We provide a comparison of the top five runs that achieved the highest average precision scores. In addition, we used questionnaire-based evaluation to measure the effectiveness of the ranking task.
The results indicated that the proposed model achieved a higher average precision in comparison with top-scored runs submitted to TREC-9; the improvement of our model over other methods is statistically significant (p-value <0.0001). Furthermore, a questionnaire-based experiment showed that the proposed model performed quite well in ranking retrieved documents according to their topics.
生物医学信息学中的一个主要问题是医疗和保健信息的上下文检索和排序。本文提出了一种从医学和临床文档中提取语义关系的模型。目的是在用户输入最少的情况下最大化上下文检索和排序性能。
我们开发并评估了一种依赖于多特征相似性模型的医学搜索引擎。索引的文档表示为一个网络，反映了文档之间的语义关系，以评估主题排名。
评估测量包括以下内容：召回率、精度和 R 精度。我们使用 OHSUMED 集合来评估我们在 TREC-9 中提交的工作。我们提供了对获得最高平均精度得分的前五个运行的比较。此外，我们还使用基于问卷调查的评估来衡量排序任务的有效性。
结果表明，与提交给 TREC-9 的得分最高的运行相比，所提出的模型在平均精度方面有了显著提高；与其他方法相比，我们的模型的改进具有统计学意义（p 值<0.0001）。此外，基于问卷调查的实验表明，根据主题对检索到的文档进行排序，所提出的模型表现相当出色。
