Drug Specification Named Entity Recognition Base on BiLSTM-CRF Model

Li, Weiyan; Song, Wenai; Jia, Xin-Hong; Yang, Ji-Jiang; Wang, Qing; Lei, Yi; Huang, Ke; Li, Jun; Yang, Ting

doi:10.1109/compsac.2019.10244

Cited by 12 publications

(7 citation statements)

References 2 publications

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“…In addition, from LSTM to BiLSTM, the overall F1 value increased from 93.09% to 93.66%, which is an apparent improvement. This is because LSTM only extracts one-way features of sequences, resulting in the lack of many useful features that make sense for sequence labels, whereas BiLSTM can extracts the features from both forward and backward directions of the sequence, so as to obtain the knowledge more comprehensively and thereby achieve better performance [32].…”

Section: Resultsmentioning

confidence: 99%

Named Entity Recognition of Traditional Chinese Medicine Patents Based on BiLSTM‐CRF

Deng

Chen

2021

Wireless Communications and Mobile Computing

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With the growing popularity of traditional Chinese medicine (TCM) in the world and the increasing awareness of intellectual property protection, the number of TCM patent application is growing year by year. TCM patents contain rich medical, legal, and economic information. Effective text mining of TCM patents is of great theoretical and practical significance (e.g., the R&D of new medicines, patent infringement litigation, and patent acquisition). Named entity recognition (NER) is a fundamental task in natural language processing and a crucial step before indepth analysis of TCM patent. In this paper, a method combining Bidirectional Long Short-Term Memory neural network with Conditional Random Field (BiLSTM-CRF) is proposed to automatically recognize entities of interest (i.e., herb names, disease names, symptoms, and therapeutic effects) from the abstract texts of TCM patents. By virtue of the capabilities of deep learning methods, the semantic information in the context can be learned without feature engineering. Experiments show that the BiLSTM-CRF-based method provides superior performance in comparison with various baseline methods.

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Section: Resultsmentioning

confidence: 99%

Named Entity Recognition of Traditional Chinese Medicine Patents Based on BiLSTM‐CRF

Deng

Chen

2021

Wireless Communications and Mobile Computing

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“…BiLSTM-CRF : This model was used by Li, et al In order to realize automatic recognition and extraction of entities in unstructured medical texts, a model combining language model conditional random field algorithm (CRF) and Bi-directional Long Short-term Memory networks (BiLSTM) is proposed [ 21 ].…”

Section: Resultsmentioning

confidence: 99%

Enhanced character-level deep convolutional neural networks for cardiovascular disease prediction

Zhang

Qiu

Yang

et al. 2020

BMC Med Inform Decis Mak

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Background Electronic medical records contain a variety of valuable medical information for patients. So, when we are able to recognize and extract risk factors for disease from EMRs of patients with cardiovascular disease (CVD), and are able to use them to predict CVD, we have the ability to automatically process clinical texts, resulting in an improved accuracy of supporting doctors for the clinical diagnosis of CVD. In the case where CVD is becoming more worldwide, predictive CVD based on EMRs has been studied by many researchers to address this important aspect of improving diagnostic efficiency. Methods This paper proposes an Enhanced Character-level Deep Convolutional Neural Networks (EnDCNN) model for cardiovascular disease prediction. Results On the manually annotated Chinese EMRs corpus, our risk factor identification extraction model achieved 0.9073 of F-score, our prediction model achieved 0.9516 of F-score, and the prediction result is better than the most previous methods. Conclusions The character-level model based on text region embedding can well map risk factors and their labels as a unit into a vector, and downsampling plays a crucial role in improving the training efficiency of deep CNN. What’s more, the shortcut connections with pre-activation used in our model architecture implements dimension-matching free in training.

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“…BiLSTM-CRF: This model was used by Li et al In order to realize automatic recognition and extraction of entities in unstructured medical texts, a model combining language model conditional random field algorithm (CRF) and Bi-directional Long Shortterm Memory networks (BiLSTM) is proposed [24]. SVM: This model was used by S. Menaria et al As a traditional machine learning method, the support vector machine algorithm performs well in [25].…”

Section: Models and Parametersmentioning

confidence: 99%

Deep Cardiovascular Disease Prediction with Risk Factors Powered Bi-attention

Qiu

Zhang

Qin

et al. 2020

Neural Information Processing

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Background Cardiovascular disease (CVD), as a chronic disease, has been perplexing human beings and is one of the serious diseases endangering life and health. Therefore, using the electronic medical record information of patients to automatically predict CVD has important application value in intelligent auxiliary diagnosis and treatment, and is a hot issue in intelligent medical research. In recent years, attention mechanism has been successfully extended to various tasks of natural language processing. Typically, these methods use attention to focus on a small part of the context and summarize it with a fixed-size vector, coupling attention in time, and/or often forming a uni-directional attention. Methods In this paper, we propose a CVD risk factors powered bi-directional attention (RFPBiA) network, which is a multi-stage hierarchical process, representing information fusion at different granularity levels, and uses the bi-directional attention to obtain the text representation of risk factors without early aggregation. Results The experimental results show that the proposed method can obviously improve the performance of CVD prediction, and the F-score reaches 0.9424, which is better than the existing related methods. Conclusions We propose to extract the risk factors leading to CVD by using the existing mature entity recognition technology, which provides a new idea for disease prediction tasks. Moreover, the memory-less attention mechanism in both directions in our proposed prediction model of RFPBiA can fuse the character sequence and the risk factors contained in the electronic medical record text to predict CVD.

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Drug Specification Named Entity Recognition Base on BiLSTM-CRF Model

Cited by 12 publications

References 2 publications

Named Entity Recognition of Traditional Chinese Medicine Patents Based on BiLSTM‐CRF

Named Entity Recognition of Traditional Chinese Medicine Patents Based on BiLSTM‐CRF

Enhanced character-level deep convolutional neural networks for cardiovascular disease prediction

Deep Cardiovascular Disease Prediction with Risk Factors Powered Bi-attention

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