Department of Biomedical Informatics, University of California, San Diego, United States.
J Biomed Inform. 2018 Feb;78:43-53. doi: 10.1016/j.jbi.2017.12.007. Epub 2017 Dec 19.
Modern medical information systems enable the collection of massive temporal health data. Albeit these data have great potentials for advancing medical research, the data exploration and extraction of useful knowledge present significant challenges. In this work, we develop a new pattern matching technique which aims to facilitate the discovery of clinically useful knowledge from large temporal datasets. Our approach receives in input a set of temporal patterns modeling specific events of interest (e.g., doctor's knowledge, symptoms of diseases) and it returns data instances matching these patterns (e.g., patients exhibiting the specified symptoms). The resulting instances are ranked according to a significance score based on the p-value. Our experimental evaluations on a real-world dataset demonstrate the efficiency and effectiveness of our approach.
现代医学信息系统能够收集大量的时间健康数据。尽管这些数据在推进医学研究方面具有巨大的潜力，但数据的探索和有用知识的提取仍然存在很大的挑战。在这项工作中，我们开发了一种新的模式匹配技术，旨在从大型时间数据集发现临床有用的知识。我们的方法接收一组时间模式作为输入，这些模式用于对特定感兴趣的事件进行建模（例如，医生的知识、疾病的症状），并返回匹配这些模式的数据实例（例如，表现出特定症状的患者）。根据基于 p 值的显著分数对得到的实例进行排序。我们在真实数据集上的实验评估证明了我们方法的效率和有效性。
