针对临床电子病历中因缺少中文注释而导致实体识别性能差的问题,本文提出了一种结合BART融合技术的多医学实体识别方法F-MNER,Bi-LSTM,和CRF。首先,清洗后,编码,分割电子病历,使用双向自回归变换器(BART)模型动态融合所获得的语义表示。然后,使用双向长短期记忆(Bi-LSTM)网络捕获顺序信息。最后,条件随机场(CRF)用于解码和输出多任务实体识别。在CCKS2019数据集上进行了实验,具有microavgPrecision,宏平均召回,加权平均精度达到0.880、0.887和0.883,微平均F1分数,宏平均F1分数,加权平均F1评分分别达到0.875、0.876和0.876。与现有模型相比,我们的方法在三个评估指标(微观平均值,宏观平均值,加权平均)在相同的数据集条件下。在加权平均的情况下,精度,回想一下,F1得分为19.64%,15.67%,分别比现有BERT-BiLSTM-CRF模型高17.58%。使用我们的MF-MNER对实际的临床数据集进行实验,精度,回想一下,在micro-avg评估机制下,F1得分分别为0.638、0.825和0.719。精度,回想一下,在宏观平均评估机制下,F1得分分别为0.685、0.800和0.733。精度,回想一下,在加权平均评估机制下,F1得分分别为0.647、0.825和0.722。以上结果表明,我们的方法MF-MNER可以集成BART的优点,Bi-LSTM,和CRF层,显著提高下游命名实体识别任务的性能,只需少量注释,在召回得分方面取得了出色的表现,具有一定的现实意义。复制本文结果的源代码和数据集可在https://github.com/xfwang1969/MF-MNER获得。
To 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, with micro avg Precision, macro avg Recall, weighted avg Precision reaching 0.880, 0.887, and 0.883, and micro 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, the Precision, Recall, and F1-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, the Precision, Recall, and F1-score are 0.638, 0.825, and 0.719 under the micro-avg evaluation mechanism. The Precision, Recall, and F1-score are 0.685, 0.800, and 0.733 under the macro-avg evaluation mechanism. The Precision, Recall, and F1-score are 0.647, 0.825, and 0.722 under the weighted avg evaluation mechanism. The above results show that our method MF-MNER can integrate the advantages of BART, Bi-LSTM, and
CRF layers, significantly improving the performance of downstream named entity recognition tasks with a small amount of annotation, and achieving excellent performance in terms of recall score, which has certain practical significance. Source code and datasets to reproduce the results in this paper are available at https://github.com/xfwang1969/MF-MNER .