IEEE J Biomed Health Inform. 2019 Sep;23(5):2138-2147. doi: 10.1109/JBHI.2018.2877255. Epub 2018 Oct 22.
Exploring the temporal relationship among events in patient electronic medical records (EMR) is an important problem in biomedical informatics and the results can reveal patients' impending disease conditions. In this paper, we investigate the problem of mining patterns from a sequence of point events, i.e., we only have the information on when the event happens but no duration or numerical value available. We propose a whole pipeline, including event preprocessing, pattern mining, and outcome analysis to mine the patterns and evaluate their effectiveness and discriminative power. Finally, we treat those mined patterns as additional features and evaluate them in a predictive modeling task for the early detection of congestive heart failure. On a real-world EMR data warehouse, we found that by adding those sequential pattern features, the prediction performance could be significantly improved approximately 0.1.
探索患者电子病历（EMR）中事件之间的时间关系是生物医学信息学中的一个重要问题，其结果可以揭示患者即将出现的疾病状况。在本文中，我们研究了从一系列点事件中挖掘模式的问题，即我们仅拥有有关事件发生时间的信息，但没有可用的持续时间或数值。我们提出了一个完整的管道，包括事件预处理、模式挖掘和结果分析，以挖掘模式并评估其有效性和区分能力。最后，我们将那些挖掘出的模式视为附加特征，并在用于心力衰竭早期检测的预测建模任务中对其进行评估。在一个真实的 EMR 数据仓库中，我们发现通过添加这些序列模式特征，预测性能可以显著提高约 0.1。
