Time Recognition of Chinese Electronic Medical Record of Depression Based on Conditional Random Field

Shaofu Lin; Yuanyuan Zhao; Zhisheng Huang

doi:10.1007/978-3-030-37078-7_15

Time Recognition of Chinese Electronic Medical Record of Depression Based on Conditional Random Field

Shaofu Lin
, Yuanyuan Zhao
, Zhisheng Huang^*

^*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

As an important entity in medical texts, time information plays an important role in structuring medical information and supporting clinical decision-making. In this paper, time expressions in Chinese electronic medical record text of depression are studied. The method combines regular expressions with Conditional random fields (CRFs) to recognize time expressions in Chinese electronic medical records. The test data are realistic electronic medical records of depression provided by a hospital in Beijing. The proposed method uses regular expressions to initially recognize the explicit time expression in the text, and adds a dictionary of common drugs and symptoms of depression to the word segmentation, which increases the accuracy of word segmentation. External dictionary features are optimized, and dictionaries are divided into time modifier dictionary, time representation dictionary and event dictionary, which effectively improve the accuracy and recall rate of conditional random field recognition results. Experiments show that the accuracy and recall rate of this method are 96.75% and 93.33% respectively.

Original language	English
Title of host publication	Brain Informatics
Subtitle of host publication	12th International Conference, BI 2019, Haikou, China, December 13–15, 2019, Proceedings
Editors	Peipeng Liang, Vinod Goel, Chunlei Shan
Publisher	Springer
Pages	149-158
Number of pages	10
ISBN (Electronic)	9783030370787
ISBN (Print)	9783030370770
DOIs	https://doi.org/10.1007/978-3-030-37078-7_15
Publication status	Published - 2019
Event	12th International Conference on Brain Informatics, BI 2019 - Haikou, China Duration: 13 Dec 2019 → 15 Dec 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11976 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Brain Informatics, BI 2019
Country/Territory	China
City	Haikou
Period	13/12/19 → 15/12/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

Keywords

Conditional random field
Electronic medical record for depression
Named entity recognition
Time representation regular expression

Access to Document

10.1007/978-3-030-37078-7_15

Time Recognition of Chinese Electronic Medical Record of Depression Based on Conditional Random FieldFinal published version, 551 KBLicence: Article 25fa Dutch Copyright Act

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Lin, S., Zhao, Y., & Huang, Z. (2019). Time Recognition of Chinese Electronic Medical Record of Depression Based on Conditional Random Field. In P. Liang, V. Goel, & C. Shan (Eds.), Brain Informatics : 12th International Conference, BI 2019, Haikou, China, December 13–15, 2019, Proceedings (pp. 149-158). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11976 LNAI). Springer. https://doi.org/10.1007/978-3-030-37078-7_15

Lin, Shaofu ; Zhao, Yuanyuan ; Huang, Zhisheng. / Time Recognition of Chinese Electronic Medical Record of Depression Based on Conditional Random Field. Brain Informatics : 12th International Conference, BI 2019, Haikou, China, December 13–15, 2019, Proceedings. editor / Peipeng Liang ; Vinod Goel ; Chunlei Shan. Springer, 2019. pp. 149-158 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Time Recognition of Chinese Electronic Medical Record of Depression Based on Conditional Random Field",

abstract = "As an important entity in medical texts, time information plays an important role in structuring medical information and supporting clinical decision-making. In this paper, time expressions in Chinese electronic medical record text of depression are studied. The method combines regular expressions with Conditional random fields (CRFs) to recognize time expressions in Chinese electronic medical records. The test data are realistic electronic medical records of depression provided by a hospital in Beijing. The proposed method uses regular expressions to initially recognize the explicit time expression in the text, and adds a dictionary of common drugs and symptoms of depression to the word segmentation, which increases the accuracy of word segmentation. External dictionary features are optimized, and dictionaries are divided into time modifier dictionary, time representation dictionary and event dictionary, which effectively improve the accuracy and recall rate of conditional random field recognition results. Experiments show that the accuracy and recall rate of this method are 96.75\% and 93.33\% respectively.",

keywords = "Conditional random field, Electronic medical record for depression, Named entity recognition, Time representation regular expression",

author = "Shaofu Lin and Yuanyuan Zhao and Zhisheng Huang",

year = "2019",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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editor = "Peipeng Liang and Vinod Goel and Chunlei Shan",

booktitle = "Brain Informatics",

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}

Lin, S, Zhao, Y & Huang, Z 2019, Time Recognition of Chinese Electronic Medical Record of Depression Based on Conditional Random Field. in P Liang, V Goel & C Shan (eds), Brain Informatics : 12th International Conference, BI 2019, Haikou, China, December 13–15, 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11976 LNAI, Springer, pp. 149-158, 12th International Conference on Brain Informatics, BI 2019, Haikou, China, 13/12/19. https://doi.org/10.1007/978-3-030-37078-7_15

Time Recognition of Chinese Electronic Medical Record of Depression Based on Conditional Random Field. / Lin, Shaofu; Zhao, Yuanyuan; Huang, Zhisheng.
Brain Informatics : 12th International Conference, BI 2019, Haikou, China, December 13–15, 2019, Proceedings. ed. / Peipeng Liang; Vinod Goel; Chunlei Shan. Springer, 2019. p. 149-158 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11976 LNAI).

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Time Recognition of Chinese Electronic Medical Record of Depression Based on Conditional Random Field

AU - Lin, Shaofu

AU - Zhao, Yuanyuan

AU - Huang, Zhisheng

PY - 2019

Y1 - 2019

N2 - As an important entity in medical texts, time information plays an important role in structuring medical information and supporting clinical decision-making. In this paper, time expressions in Chinese electronic medical record text of depression are studied. The method combines regular expressions with Conditional random fields (CRFs) to recognize time expressions in Chinese electronic medical records. The test data are realistic electronic medical records of depression provided by a hospital in Beijing. The proposed method uses regular expressions to initially recognize the explicit time expression in the text, and adds a dictionary of common drugs and symptoms of depression to the word segmentation, which increases the accuracy of word segmentation. External dictionary features are optimized, and dictionaries are divided into time modifier dictionary, time representation dictionary and event dictionary, which effectively improve the accuracy and recall rate of conditional random field recognition results. Experiments show that the accuracy and recall rate of this method are 96.75% and 93.33% respectively.

AB - As an important entity in medical texts, time information plays an important role in structuring medical information and supporting clinical decision-making. In this paper, time expressions in Chinese electronic medical record text of depression are studied. The method combines regular expressions with Conditional random fields (CRFs) to recognize time expressions in Chinese electronic medical records. The test data are realistic electronic medical records of depression provided by a hospital in Beijing. The proposed method uses regular expressions to initially recognize the explicit time expression in the text, and adds a dictionary of common drugs and symptoms of depression to the word segmentation, which increases the accuracy of word segmentation. External dictionary features are optimized, and dictionaries are divided into time modifier dictionary, time representation dictionary and event dictionary, which effectively improve the accuracy and recall rate of conditional random field recognition results. Experiments show that the accuracy and recall rate of this method are 96.75% and 93.33% respectively.

KW - Conditional random field

KW - Electronic medical record for depression

KW - Named entity recognition

KW - Time representation regular expression

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U2 - 10.1007/978-3-030-37078-7_15

DO - 10.1007/978-3-030-37078-7_15

M3 - Conference contribution

AN - SCOPUS:85078537614

SN - 9783030370770

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 149

EP - 158

BT - Brain Informatics

A2 - Liang, Peipeng

A2 - Goel, Vinod

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PB - Springer

T2 - 12th International Conference on Brain Informatics, BI 2019

Y2 - 13 December 2019 through 15 December 2019

ER -

Lin S, Zhao Y, Huang Z. Time Recognition of Chinese Electronic Medical Record of Depression Based on Conditional Random Field. In Liang P, Goel V, Shan C, editors, Brain Informatics : 12th International Conference, BI 2019, Haikou, China, December 13–15, 2019, Proceedings. Springer. 2019. p. 149-158. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2019 Dec 5. doi: 10.1007/978-3-030-37078-7_15

Time Recognition of Chinese Electronic Medical Record of Depression Based on Conditional Random Field

Abstract

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Keywords

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