research-article

A BiLSTM-CRF Method to Chinese Electronic Medical Record Named Entity Recognition

Authors:

Bin Ji,

Rui Liu,

ShaSha Li,

JinTao Tang,

Jie Yu,

Qian Li,

WeiSang XuAuthors Info & Claims

ACAI '18: Proceedings of the 2018 International Conference on Algorithms, Computing and Artificial Intelligence

Article No.: 48, Pages 1 - 6

https://doi.org/10.1145/3302425.3302465

Published: 21 December 2018 Publication History

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Abstract

With the application of electronic medical records in medical field, more and more people are paying attention to how to use these data efficiently. In this paper, the BiLSTM-CRF model is applied to Chinese electronic medical records to recognize related named entities in these records. For the characteristics of Chinese electronic medical records, firstly, the one-hot vector of each word is obtained in units of sentences. Secondly, map one-hot vector to a low-dimensional dense word vector. Thirdly, word vector is used as the input of the BiLSTM layer to achieve automatic extraction of sentence features. Finally, the CRF layer performs sequence-level labeling of sentences. In addition, drug dictionary and post-correction rules are added to correct the segmentation error of entity boundary, to improve recognition accuracy of related named entities. The F1 value of this method on a given test data set is 87.68%.

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A BiLSTM-CRF Method to Chinese Electronic Medical Record Named Entity Recognition

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Published In

ACAI '18: Proceedings of the 2018 International Conference on Algorithms, Computing and Artificial Intelligence

December 2018

460 pages

ISBN:9781450366250

DOI:10.1145/3302425

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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The Hong Kong Polytechnic: The Hong Kong Polytechnic University
City University of Hong Kong: City University of Hong Kong

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Association for Computing Machinery

New York, NY, United States

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Published: 21 December 2018

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ACAI 2018

ACAI 2018: 2018 International Conference on Algorithms, Computing and Artificial Intelligence

December 21 - 23, 2018

Sanya, China

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ACAI '18 Paper Acceptance Rate 76 of 192 submissions, 40%;

Overall Acceptance Rate 173 of 395 submissions, 44%

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Xu WWang LZhang MZhu JYan JWu Q(2024)A joint entity Relation Extraction method for document level Traditional Chinese Medicine textsArtificial Intelligence in Medicine10.1016/j.artmed.2024.102915154(102915)Online publication date: Aug-2024
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Wang BJiang F(2024)Named Entity Recognition Using EHealth-BiLSTM-CRF Combine with Multi-head Self-attention for Chinese Medical InformationWeb Information Systems and Applications10.1007/978-981-97-7707-5_37(451-462)Online publication date: 11-Sep-2024
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Research on Named Entity Recognition of Traditional Chinese Medicine Electronic Medical Records

Medical Knowledge Attention Enhanced Neural Model for Named Entity Recognition in Chinese EMR

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