Named entity recognition based on bidirectional long short-term memory combined with case report form

doi:10.3969/j.issn.2095-4344.0302

Abstract

Abstract:

BACKGROUND: Electronic medical record (EMR) is an important source of medical source, reflecting medical knowledge. There are patient clinical features in EMR, which enables decision support system and precision medicine.
OBJECTIVE: To extract important medical entities of EMR using information extraction, and to discover hepatocellular carcinoma knowledge.
METHODS: The EMR database of a Grade-A Tertiary hospital in Guangdong Province was used. We retrieved clinical records (18 542 sentences) of 240 patients suffering from hepatocellular carcinoma, including admission notes and discharge summaries. The records were remarked according to the predetermined standards. Totally 180 patients’ records (13 839 sentences) were selected randomly for training and 60 patients’ records (4 703 sentences) were remained for testing. Bidirectional long short-term memory combined with case report form was used to identify the model. The performance of NER systems was evaluated on the test datasets, and precision, recall, F1 of strict matching were caculated.
RESULTS AND CONCLUSION: Evaluation on the dataset showed that an F1-measure of 0.853 5 was for admission, F1-measure of 0.726 5 was for the discharge summaries, and an overall F1-measure was 0.805 2. In this study, we have achieved the auto-name entity identification model of EMR, but the accuracy of entity extraction needs further investigation.

中国组织工程研究杂志出版内容重点：组织构建；骨细胞；软骨细胞；细胞培养；成纤维细胞；血管内皮细胞；骨质疏松；组织工程

Key words: Medical Records Systems, Computerized, Neural Networks (Computer), Liver Neoplasms, Tissue Engineering

CLC Number:

r318

Yang Hong-mei1, Li Lin2, Yang Ri-dong1, Zhou Yi1, 2. Named entity recognition based on bidirectional long short-term memory combined with case report form[J]. Chinese Journal of Tissue Engineering Research, 2018, 22(20): 3237-3242.

Figures/Tables 3

References

[1] Lossio-Ventura JA, Hogan W, Modave F, et al.Towards an obesity-cancer knowledge base: Biomedical entity identification and relation detection. Proceedings (IEEE Int Conf Bioinformatics Biomed). 2016;2016:1081-1088.

[2] Kohane IS.Using electronic health records to drive discovery in disease genomics.Nat Rev Genet. 2011;12(6): 417-428.

[3] Razavian N, Blecker S, Schmidt AM, et al.Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors.Big Data. 2015;3(4): 277-287.

[4] Rotmensch M, Halpern Y, Tlimat A, et al.Learning a Health Knowledge Graph from Electronic Medical Records.Sci Rep. 2017;7(1):5994.

[5] Small AM, Kiss DH, Zlatsin Y, et al.Text mining applied to electronic cardiovascular procedure reports to identify patients with trileaflet aortic stenosis and coronary artery disease.J Biomed Inform.2017;72:77-84.

[6] Payne TH, Corley S, Cullen TA,et al.Report of the AMIA EHR-2020 Task Force on the status and future direction of EHRs.J Am Med Inform Assoc.2015;22(5):1102-1110.

[7] Wang Y, Wang L, Rastegar-Mojarad M, et al.Clinical Information Extraction Applications: A Literature Review.J Biomed Inform.2017.

[8] Jensen PB, Jensen LJ, Brunak S.Brunak,Mining electronic health records: towards better research applications and clinical care.Nat Rev Genet.2012;13(6): 395-405.

[9] 何林娜,杨志豪,林鸿飞,等.基于特征耦合泛化的药名实体识别[J].中文信息学报, 2014,28(2):72-77.

[10] 栗伟,赵大哲,李博,等.CRF与规则相结合的医学病历实体识别[J].计算机应用研究, 2015,32(4):1082-1086.

[11] Lei J, Tang B, Lu X,et al.A comprehensive study of named entity recognition in Chinese clinical text.J Am Med Inform Assoc.2014;21(5):808-814.

[12] Hu JL,Shi X, Liu ZJ,et al.HITSZ_CNER: A hybrid system for entity recognition from Chinese clinical text.

[13] Wu J,Hu X, Zhao R,et al.Clinical Named Entity Recognition via Bi-directional LSTM-CRF Model.

[14] Xia Y, Wang Q.Clinical Named Entity Recognition: ECUST in the CCKS-2017 Shared Task 2.

[15] Kingma DP,Ba J.Adam: A Method for XStochastic Optimization. Computer Science.2014.

[16] fssjy. “Jieba” (Chinese for “to stutter”) Chinese text segmentation: built to be the best Python Chinese word segmentation module.https://pypi.python.org/pypi/jieba/.

[17] Mikolov T,Chen K,Corrado G,et al.Efficient Estimation of Word XRepresentations in Vector Space.Computer Science. 2013.

[18] Huang Z, Xu W, Yu K.Bidirectional LSTM-CRF Models for Sequence Tagging.Computer Science.2015.

[19] Cogswell M,AhXmed F,Girshick R,et al.Reducing Overfitting in Deep Networks by Decorrelating Representations. Computer Science.2015.

[20] Srivastava N,Hinton G,Krizhevsky A,et al.Dropout:a simple way to prevent neural networks from overfitting.Journal of Machine Learning Research.2014;15(1): 1929-1958.

[21] Uzuner Ö,South BR,Shen S,et al.2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text. J Am Med Inform Assoc. 2011;18(5):552-556.

[22] 杨红梅,田翔华,周毅.电子病历对基于知识网络的精准医学的支撑及模式研究[J].中国数字医学,2017,12(8):29-31+75.

[23] Jonnalagadda SR,Adupa AK,Garg RP,et al.Text Mining of the Electronic Health Record: An Information Extraction Approach for Automated Identification and Subphenotyping of HFpEF Patients for Clinical Trials.J Cardiovasc Transl Res. 2017;10(3): 313-321.

[24] Kumar V,Stubbs A,Shaw S,et al.Creation of a new longitudinal corpus of clinical narratives. J Biomed Inform. 2015;58 Suppl:S6-S10.

[25] Kübler S,Zinsmeister H.Corpus linguistics and linguistically annotated corpora. Bloomsbury Academic.2015.

[26] 杨锦锋,关毅,何彬,等.中文电子病历命名实体和实体关系语料库构建[J].软件学报,2016,27(11):2725-2746.

[27] Zhang S, Kang T, Zhang X, et al.Speculation detection for Chinese clinical notes: Impacts of word segmentation and embedding models.J Biomed Inform.2016;60:334-341.

[28] Lishuang Li, Liuke Jin, Yuxin Jiang, et al.Recognizing Biomedical Named Entities Based on the Sentence Vector/Twin Word Embeddings Conditioned Bidirectional LSTM. 2016: Springer International Publishing.

[29] Wang Y, Wang L, Rastegar-Mojarad M, et al. Clinical information extraction applications: A literature review. J Biomed Inform. 2018;77:34-49.

[30] Varone M, Varone M, Varone M, et al. Conditional random fields with semantic enhancement for named-entity recognition. in International Conference on Web Intelligence, Mining and Semantics. 2017.

[31] Shao Y, Hardmeier C, Tiedemann J, et al. Character-based Joint Segmentation and POS Tagging for Chinese using Bidirectional RNN-CRF.2017.

[32] Pham TH, Lehong P.End-to-end Recurrent Neural Network Models for Vietnamese Named Entity Recognition: Word-level vs. Character-level.2017.

[33] Gridach M.Character-Level Neural Network for Biomedical Named Entity Recognition. J Biomed Inform. 2017;70:85-91.