A Pattern-Based Method for Medical Entity Recognition From Chinese Diagnostic Imaging Text - Liang, Zihong; Chen, Junjie; Xu, Zhaopeng; Chen, Yuyang; Hao, Tianyong (Frontiers in Artificial Intelligence, 2019)
Background: The identification of medical entities and relations from electronic medical records is a fundamental research for medical informatics. However, extracting valuable knowledge from these records is a challenging work due to its high complexity. The accurate identification of entity and relation is still an open research problem in medical information extraction. Methods: A pattern-based method for extracting certain tumor related entities and attributes from Chinese unstructured diagnostic imaging text is proposed. This method is a composition of three steps. Firstly, an algorithm based on keyword matching is designed to obtain the primary sites of tumors. Then a set of regular expressions is applied to identify primary tumor size information. Finally, a set of rules is defined to acquire metastatic sites of tumors. Results: Our method achieves a recall of 0.697, a precision of 0.825 and an F1 score of 0.755 using an overall weighted metric. For each of extraction tasks, the F1 scores are 0.784, 0.822 and 0.740, respectively. Conclusions: The method is proved to be stable and robust with different amounts of testing data. It achieves a comparatively high performance in CHIP 2018 open challenge, demonstrating its effectiveness in extracting tumor related entities from Chinese diagnostic imaging text.