Clinical named entity recognition from Chinese electronic medical records using a double-layer annotation model combining a domain dictionary with CRF
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摘要: 醫療實體識別是電子病歷文本信息抽取的基本任務。針對中文電子病歷文本復合實體較多、實體長度較長、句子成分缺失嚴重、實體邊界不清的語言特點以及標注語料難以獲取的現狀，提出了一種基于領域詞典和條件隨機場（CRF）的雙層標注模型。該模型通過對外部資源的統計分析構建醫療領域詞典，再結合條件隨機場，進行了兩次不同粒度的標注，將領域詞典識別的準確性和機器學習的自動性融為一體，從中文電子病歷文本中識別出疾病、癥狀、藥品、操作四類醫療實體。該模型在測試數據中的宏精確率為96.7%、宏召回率為97.7%、宏F1值為97.2%。同時對比分析了采用注意力機制的深度神經網絡的識別效果，因受到領域數據集大小的限制，在該測試數據集中后者表現不佳。實驗結果表明了該雙層標注模型對中文醫療實體識別的高效性。Abstract: As a document recorded by professional medical personnel, electronic medical records contain a large and important clinical resource. How to use a large amount of potential information in electronic medical records has become one of the major research directions. Chinese electronic medical records are knowledge-intensive, in which the data has considerable research value. However, they have more complex entities because of the language features of Chinese, and the composite entity is long. These sentences components in the text are missing. Moreover, the boundaries of clinical entities are often unclear. Labeling corpus is a job that requires a great deal of manpower because of the technical language used in a given text. Therefore, the recognition of Chinese clinical named entities is a hard problem. Considering these characteristics of Chinese electronic medical records, this paper proposed a double-layer annotation model that combined with a domain dictionary and conditional random field (CRF). A medical domain dictionary was constructed by statistical analysis method, and combined with CRF to mark two different granularity labeling operations. The manually constructed medical domain dictionary has extremely high accuracy for the recognition of registered words, and machine learning could automatically recognize unregistered words. This work integrated the two aspects based on these advantages. With the proposed method, diseases, symptoms, drugs, and operations could be recognized from Chinese electronic medical records. Using the test dataset, the Macro-P with 96.7%, the Macro-R with 97.7% and the Macro-F1 with 97.2% were obtained. The recognition performance of the proposed method was greatly improved compared with that of a single-layer model. The recognition effect of deep neural network with attention was also analyzed, which did not perform well due to the size of the domain dataset. The experimental results show the efficiency of the double-layer annotation model for the named entity recognition of Chinese electronic medical records.
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圖 2 DLAM與BiLSTM-Attention-CRF實體級別精確率對比
Figure 2. DLAM and BiLSTM-Attention-CRF precision comparison on entity
圖 3 DLAM與BiLSTM-Attention-CRF實體級別召回率對比
Figure 3. DLAM and BiLSTM-Attention-CRF recall comparison on entity
表 1 訓練集、測試集實體分布情況
Table 1. Distribution of entities among the training set and the test set
Dataset Diseases Symptoms Drugs Operations Total Training set 701 2648 546 2138 6033 Test set 273 1043 208 918 2442 
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表 2 領域詞典構成情況
Table 2. Distribution among the domain dictionary
Type Diseases Symptoms Operations Drugs Keywords Organs Location Privative Amount 1212 934 611 777 30 351 16 12
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表 3 CRF對比實驗結果
Table 3. Comparison experiment results of CRF
% Model Marco-P Marco-R Marco-F1 Baseline（Single-layer CRF） 83.3 68.1 68.1 DLAM 96.7 97.7 97.2
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表 4 BiLSTM-Attention-CRF對比實驗結果
Table 4. Comparison experiment results of BiLSTM-Attention-CRF
% Different characters embedding Marco-P Marco-R Marco-F1 Randomly initializes embedding 69.52 69.70 69.38 50-dimension embedding 53.42 54.31 53.74 150-dimension embedding 73.43 77.85 75.54 300-dimension embedding 55.36 61.03 57.88
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