Medical Named Entity Extraction from Chinese Resident Admit Notes Using Character and Word Attention-Enhanced Neural Network

Abstract

The resident admit notes (RANs) in electronic medical records (EMRs) is first-hand information to study the patient’s condition. Medical entity extraction of RANs is an important task to get disease information for medical decision-making. For Chinese electronic medical records, each medical entity contains not only word information but also rich character information. Effective combination of words and characters is very important for medical entity extraction. We propose a medical entity recognition model based on a character and word attention-enhanced (CWAE) neural network for Chinese RANs. In our model, word embeddings and character-based embeddings are obtained through character-enhanced word embedding (CWE) model and Convolutional Neural Network (CNN) model. Then attention mechanism combines the character-based embeddings and word embeddings together, which significantly improves the expression ability of words. The new word embeddings obtained by the attention mechanism are taken as the input to bidirectional long short-term memory (BI-LSTM) and conditional random field (CRF) to extract entities. We extracted nine types of key medical entities from Chinese RANs and evaluated our model. The proposed method was compared with two traditional machine learning methods CRF, support vector machine (SVM), and the related deep learning models. The result shows that our model has better performance, and the result of our model reaches 94.44% in the F1-score.

Automatic knowledge extraction from Chinese electronic medical records and rheumatoid arthritis knowledge graph construction

“…Cheng et al ( 28 ) applied a BiLSTM-CRF model in a clinical NER task and achieved an F1-score of 87.00%. Gao et al ( 29 ) proposed a medical entity recognition model based on a character and word attention-enhanced neural network for Chinese resident admission notes and achieved an F1-score of 94.44%. Yin et al ( 30 ) applied CNNs to extract radical-level semantic information, and then a self-attention mechanism was used to directly capture the dependencies between characters.…”

Systematic Literature Review on Named Entity Recognition: Approach, Method, and Application

“…[39]- [41] As seen in Figure 6, several researchers active on the topic of NER are Akasi [14,42], Cho [43,44] Gao [45,46], Qiu [47,48] Tang [49,50], Yadav [37,38], and Naixin [51,52]. Discussions about researchers who are influential on the topic of NER cannot only be based on the number of articles published by that person but can also use the number of citations.…”

Chinese Medical Entity Recognition Model Based on Character and Word Vector Fusion

“…Lee and Lu [31] developed a medical named entity recognition model based on CRF and rule-based text attention rules, and the model has achieved good performance in recognizing medical named entities in the admission records of stroke patients. Lei et al [4] used a 400point admission record and a 400-point discharge summary from the Beijing Union Medical Hospital to conduct medical entity recognition research, in which several machine learning methods, that is, support vector machines [32], maximum entropy [33], and conditional random fields, were used to conduct the named entity recognition research on medical text. e recognition result of the discharge summary had an F-value of 90.01%.…”