Background: The identification of medical entities and relations from electronic medical records is a fundamental research issue for medical informatics. However, the task of extracting valuable knowledge from these records is challenging 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 the extraction tasks, the F1 scores are 0.784, 0.822 and 0.740. Conclusions: The method proves to be stable and robust with different amounts of testing data. It achieves a comparatively high performance in the CHIP 2018 open challenge, demonstrating its effectiveness in extracting tumor-related entities from Chinese diagnostic imaging text.
Background: The identification of medical entities and relations from electronic medical records is a fundamental research issue for medical informatics. However, the task of extracting valuable knowledge from these records is challenging 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 the extraction tasks, the F1 scores are 0.784, 0.822 and 0.740. Conclusions: The method proves to be stable and robust with different amounts of testing data. It achieves a comparatively high performance in the CHIP 2018 open challenge, demonstrating its effectiveness in extracting tumor-related entities from Chinese diagnostic imaging text.