MF-MNER: Multi-models Fusion for MNER in Chinese Clinical Electronic Medical Records

AbstractTo address the problem of poor entity recognition performance caused by the lack of Chinese annotation in clinical electronic medical records, this paper proposes a multi-medical entity recognition method F-MNER using a fusion technique combining BART, Bi-LSTM, and CRF. First, after cleaning, encoding, and segmenting the electronic medical records, the obtained semantic representations are dynamically fused using a bidirectional autoregressive transformer (BART) model. Then, sequential information is captured using a bidirectional long short-term memory (Bi-LSTM) network. Finally, the conditional random field (CRF) is used to decode and output multi-task entity recognition. Experiments are performed on the CCKS2019 dataset, withmicro avg Precision,macro avg Recall,weighted avg Precision reaching 0.880, 0.887, and 0.883, andmicro avg F1-score,macro avg F1-score,weighted avg F1-score reaching 0.875, 0.876, and 0.876 respectively. Compared with existing models, our method outperforms the existing literature in three evaluation metrics (micro average,macro average,weighted average) under the same dataset conditions. In the case of weighted average, thePrecision,Recall, andF1-score are 19.64%, 15.67%, and 17.58% higher than the existing BERT-BiLSTM-CRF model respectively. Experiments are performed on the actual clinical dataset with our MF-MNER, thePrecision,Recall, andF1-score are 0.638, 0.825, and 0.719 under the micro-avg evaluation mechanism. ThePrecision,Recall, andF1...
Source: Interdisciplinary Sciences, Computational Life Sciences - Category: Bioinformatics Source Type: research