Chinese medical event detection based on event frequency distribution ratio and document consistency

Structured information especially medical events extracted from electronic medical records has extremely practical application value and play a basic role in various intelligent diagnosis and treatment systems. Fine-grained Chinese medical event detection is crucial in the process of structuring Chinese Electronic Medical Record (EMR). The current methods for detecting fine-grained Chinese medical events primarily rely on statistical machine learning and deep learning. However, they have two shortcomings: 1) they neglect to take into account the distribution characteristics of these fine-grained medical events. 2) they overlook the consistency in the distribution of medical events within each individual document. Therefore, this paper presents a fine-grained Chinese medical event detection method, which is based on event frequency distribution ratio and document consistency. To start with, a significant number of Chinese EMR texts are used to adapt the Chinese pre-training model BERT to the domain. Second, based on the fundamental features, the Event Frequency - Event Distribution Ratio (EF-DR) is devised to select distinct event information as supplementary features, taking into account the distribution of events within the EMR. Finally, using EMR document consistency within the model improves the outcome of event detection. Our experiments demonstrate that the proposed method significantly outperforms the baseline model.