A bootstrapping approach to symptom entity extraction on Chinese electronic medical records

Tianyi Qin; Yi Guan

doi:10.1007/978-3-319-47674-2_34

A bootstrapping approach to symptom entity extraction on Chinese electronic medical records

Tianyi Qin^*
, Yi Guan

^*Corresponding author for this work

School of Computer Science and Technology, Harbin Institute of Technology

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

Abstract

Symptom entities are widely distributed in Chinese electronic medical records. Previous approaches on symptom entity extraction usually extract continuous strings as symptom entities and require massive human efforts on corpus annotation. We describe the symptom entity as two-tuples of <subject, lesion> and design a soft pattern matching method to locate them in sentences in the EMR. Our bootstrapping approach which only requires a few annotated symptom tuples and it allows iterative extraction from mass electronic medical record databases without human supervision. Furthermore, the described method annotates symptom entities in EMR by the extracted tuples. Starting with 60 annotated entities, our approach reached an F value of 81.40 % in the extraction task of 3,150 entities from 992 sets of electronic medical records.

Original language	English
Title of host publication	Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data - 15th China National Conference, CCL 2016 and 4th International Symposium, NLP-NABD 2016, Proceedings
Editors	Maosong Sun, Zhiyuan Liu, Yang Liu, Hongfei Lin, Xuanjing Huang
Publisher	Springer Verlag
Pages	413-423
Number of pages	11
ISBN (Print)	9783319476735
DOIs	https://doi.org/10.1007/978-3-319-47674-2_34
State	Published - 2016
Externally published	Yes
Event	15th China National Conference on Chinese Computational Linguistics, CCL 2016 and 4th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2016 - Yantai, China Duration: 15 Oct 2016 → 16 Oct 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10035 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th China National Conference on Chinese Computational Linguistics, CCL 2016 and 4th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2016
Country/Territory	China
City	Yantai
Period	15/10/16 → 16/10/16

Keywords

Bootstrapping
Electronic medical record
Named entity extraction
Soft matching

Access to Document

10.1007/978-3-319-47674-2_34

Cite this

Qin, T., & Guan, Y. (2016). A bootstrapping approach to symptom entity extraction on Chinese electronic medical records. In M. Sun, Z. Liu, Y. Liu, H. Lin, & X. Huang (Eds.), Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data - 15th China National Conference, CCL 2016 and 4th International Symposium, NLP-NABD 2016, Proceedings (pp. 413-423). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10035 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-47674-2_34

Qin, Tianyi ; Guan, Yi. / A bootstrapping approach to symptom entity extraction on Chinese electronic medical records. Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data - 15th China National Conference, CCL 2016 and 4th International Symposium, NLP-NABD 2016, Proceedings. editor / Maosong Sun ; Zhiyuan Liu ; Yang Liu ; Hongfei Lin ; Xuanjing Huang. Springer Verlag, 2016. pp. 413-423 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A bootstrapping approach to symptom entity extraction on Chinese electronic medical records",

abstract = "Symptom entities are widely distributed in Chinese electronic medical records. Previous approaches on symptom entity extraction usually extract continuous strings as symptom entities and require massive human efforts on corpus annotation. We describe the symptom entity as two-tuples of and design a soft pattern matching method to locate them in sentences in the EMR. Our bootstrapping approach which only requires a few annotated symptom tuples and it allows iterative extraction from mass electronic medical record databases without human supervision. Furthermore, the described method annotates symptom entities in EMR by the extracted tuples. Starting with 60 annotated entities, our approach reached an F value of 81.40 \% in the extraction task of 3,150 entities from 992 sets of electronic medical records.",

keywords = "Bootstrapping, Electronic medical record, Named entity extraction, Soft matching",

author = "Tianyi Qin and Yi Guan",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 15th China National Conference on Chinese Computational Linguistics, CCL 2016 and 4th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2016 ; Conference date: 15-10-2016 Through 16-10-2016",

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doi = "10.1007/978-3-319-47674-2\_34",

language = "英语",

isbn = "9783319476735",

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

publisher = "Springer Verlag",

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editor = "Maosong Sun and Zhiyuan Liu and Yang Liu and Hongfei Lin and Xuanjing Huang",

booktitle = "Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data - 15th China National Conference, CCL 2016 and 4th International Symposium, NLP-NABD 2016, Proceedings",

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Qin, T & Guan, Y 2016, A bootstrapping approach to symptom entity extraction on Chinese electronic medical records. in M Sun, Z Liu, Y Liu, H Lin & X Huang (eds), Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data - 15th China National Conference, CCL 2016 and 4th International Symposium, NLP-NABD 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10035 LNAI, Springer Verlag, pp. 413-423, 15th China National Conference on Chinese Computational Linguistics, CCL 2016 and 4th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2016, Yantai, China, 15/10/16. https://doi.org/10.1007/978-3-319-47674-2_34

A bootstrapping approach to symptom entity extraction on Chinese electronic medical records. / Qin, Tianyi; Guan, Yi.
Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data - 15th China National Conference, CCL 2016 and 4th International Symposium, NLP-NABD 2016, Proceedings. ed. / Maosong Sun; Zhiyuan Liu; Yang Liu; Hongfei Lin; Xuanjing Huang. Springer Verlag, 2016. p. 413-423 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10035 LNAI).

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

TY - GEN

T1 - A bootstrapping approach to symptom entity extraction on Chinese electronic medical records

AU - Qin, Tianyi

AU - Guan, Yi

N1 - Publisher Copyright: © Springer International Publishing AG 2016.

PY - 2016

Y1 - 2016

N2 - Symptom entities are widely distributed in Chinese electronic medical records. Previous approaches on symptom entity extraction usually extract continuous strings as symptom entities and require massive human efforts on corpus annotation. We describe the symptom entity as two-tuples of and design a soft pattern matching method to locate them in sentences in the EMR. Our bootstrapping approach which only requires a few annotated symptom tuples and it allows iterative extraction from mass electronic medical record databases without human supervision. Furthermore, the described method annotates symptom entities in EMR by the extracted tuples. Starting with 60 annotated entities, our approach reached an F value of 81.40 % in the extraction task of 3,150 entities from 992 sets of electronic medical records.

AB - Symptom entities are widely distributed in Chinese electronic medical records. Previous approaches on symptom entity extraction usually extract continuous strings as symptom entities and require massive human efforts on corpus annotation. We describe the symptom entity as two-tuples of and design a soft pattern matching method to locate them in sentences in the EMR. Our bootstrapping approach which only requires a few annotated symptom tuples and it allows iterative extraction from mass electronic medical record databases without human supervision. Furthermore, the described method annotates symptom entities in EMR by the extracted tuples. Starting with 60 annotated entities, our approach reached an F value of 81.40 % in the extraction task of 3,150 entities from 992 sets of electronic medical records.

KW - Bootstrapping

KW - Electronic medical record

KW - Named entity extraction

KW - Soft matching

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DO - 10.1007/978-3-319-47674-2_34

M3 - 会议稿件

AN - SCOPUS:84992490559

SN - 9783319476735

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

SP - 413

EP - 423

BT - Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data - 15th China National Conference, CCL 2016 and 4th International Symposium, NLP-NABD 2016, Proceedings

A2 - Sun, Maosong

A2 - Liu, Zhiyuan

A2 - Liu, Yang

A2 - Lin, Hongfei

A2 - Huang, Xuanjing

PB - Springer Verlag

T2 - 15th China National Conference on Chinese Computational Linguistics, CCL 2016 and 4th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2016

Y2 - 15 October 2016 through 16 October 2016

ER -

Qin T, Guan Y. A bootstrapping approach to symptom entity extraction on Chinese electronic medical records. In Sun M, Liu Z, Liu Y, Lin H, Huang X, editors, Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data - 15th China National Conference, CCL 2016 and 4th International Symposium, NLP-NABD 2016, Proceedings. Springer Verlag. 2016. p. 413-423. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-47674-2_34

A bootstrapping approach to symptom entity extraction on Chinese electronic medical records

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