A multitask bi-directional RNN model for named entity recognition on Chinese electronic medical records
A multitask bi-directional RNN model for named entity recognition on Chinese electronic medical records
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DOI：
10.1186/s12859-018-2467-9
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发表时间：
2018-12-28
影响因子：
3
通讯作者：
Yu, Qiubin
中科院分区：
文献类型：
--
作者：
Chowdhury, Shanta;Dong, Xishuang;Yu, Qiubin
电子病历（electronic Medical Record， EMR）是医疗人员为提供更好的医疗服务而收集的患者医疗信息。命名实体识别（NER）是信息提取的一个分支领域，旨在识别特定的实体术语，如疾病、测试、症状、基因等。NER可以帮助医疗保健提供者和医学专家自动提取有用的信息，并避免EMR中不必要和不相关的信息。然而，有限的可用电子病历资源给采矿实体条款带来了巨大的挑战。因此，本文提出了一种多任务双向RNN模型作为数据增强的潜在解决方案，以提高有限数据下的NER性能。方法提出一种多任务双向RNN模型，用于中文电子病历实体术语的提取。该模型可分为共享层和任务特定层。首先，通过词嵌入和字符嵌入的拼接得到每个词的向量表示。然后利用双向RNN从句子中提取上下文信息。之后，所有这些层由两个不同的任务层共享，即词性标注任务层和命名实体识别任务层。这两个任务层交替训练，使得从命名实体识别任务中学习到的知识可以通过从词性标注任务中获得的知识来增强。结果从微观平均f值、宏观平均f值和准确率三个方面对模型的性能进行了评价。观察到，在所有情况下，所提出的模型都优于基线模型。例如，对放电摘要进行的实验结果表明，微观平均f分和宏观平均f分分别提高了2.41%和4.16%，整体准确率提高了5.66%。结论本文提出了一种新的多任务双向RNN模型，以提高EMR中命名实体识别的性能。实际数据集的评价结果证明了该模型的有效性。
BackgroundElectronic Medical Record (EMR) comprises patients' medical information gathered by medical stuff for providing better health care. Named Entity Recognition (NER) is a sub-field of information extraction aimed at identifying specific entity terms such as disease, test, symptom, genes etc. NER can be a relief for healthcare providers and medical specialists to extract useful information automatically and avoid unnecessary and unrelated information in EMR. However, limited resources of available EMR pose a great challenge for mining entity terms. Therefore, a multitask bi-directional RNN model is proposed here as a potential solution of data augmentation to enhance NER performance with limited data.MethodsA multitask bi-directional RNN model is proposed for extracting entity terms from Chinese EMR. The proposed model can be divided into a shared layer and a task specific layer. Firstly, vector representation of each word is obtained as a concatenation of word embedding and character embedding. Then Bi-directional RNN is used to extract context information from sentence. After that, all these layers are shared by two different task layers, namely the parts-of-speech tagging task layer and the named entity recognition task layer. These two tasks layers are trained alternatively so that the knowledge learned from named entity recognition task can be enhanced by the knowledge gained from parts-of-speech tagging task.ResultsThe performance of our proposed model has been evaluated in terms of micro average F-score, macro average F-score and accuracy. It is observed that the proposed model outperforms the baseline model in all cases. For instance, experimental results conducted on the discharge summaries show that the micro average F-score and the macro average F-score are improved by 2.41% point and 4.16% point, respectively, and the overall accuracy is improved by 5.66% point.ConclusionsIn this paper, a novel multitask bi-directional RNN model is proposed for improving the performance of named entity recognition in EMR. Evaluation results using real datasets demonstrate the effectiveness of the proposed model.