Characterizing Treatment Strategies of Intrahepatic Bile Duct Cancer by Literature Mining

Hongmei Guo; Ning Zhang; Na Hong; Lixia Yao; Wenfei Duan; Zhixiong Zhang

doi:10.1109/ICHI-W.2018.00013

Characterizing Treatment Strategies of Intrahepatic Bile Duct Cancer by Literature Mining

Hongmei Guo, Ning Zhang, Na Hong, Lixia Yao, Wenfei Duan, Zhixiong Zhang

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

Abstract

Scientific literature is arguably the most objective, comprehensive and timely data source of medical knowledge discovery and evidence based medicine, as the significant clinical outcomes are often reported in a timely fashion in scientific journals. In this project, we proposed a text mining method for acquiring treatment strategies of intrahepatic bile duct cancer from PubMed abstracts. Treatment related concepts including therapeutic procedures, medications, genes and medical devices were systematically extracted and analyzed. A term co-occurrence network was generated to identify the main topics of the latest research on treatment of intrahepatic bile duct cancer. The treatment relations about 4 groups of different concepts, including Therapeutic Procedure, Medication, Care Activity, and Medical Device, were identified and demonstrated. Our study showed that the automatic treatment strategies extraction from literature with a small amount of physicians' verification added is a feasible solution to quickly characterize the overall treatment research progress.

Original language	English (US)
Title of host publication	Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	43-48
Number of pages	6
ISBN (Electronic)	9781538667774
DOIs	https://doi.org/10.1109/ICHI-W.2018.00013
State	Published - Jul 16 2018
Externally published	Yes
Event	6th IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018 - New York, United States Duration: Jun 4 2018 → Jun 7 2018

Other

Other	6th IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018
Country	United States
City	New York
Period	6/4/18 → 6/7/18

Fingerprint

Keywords

iCCA
intrahepatic bile duct cancer
intrahepatic cholangiocarcinoma
relation extraction
text mining
treatment

ASJC Scopus subject areas

Information Systems and Management
Health Informatics

Cite this

Guo, H., Zhang, N., Hong, N., Yao, L., Duan, W., & Zhang, Z. (2018). Characterizing Treatment Strategies of Intrahepatic Bile Duct Cancer by Literature Mining. In Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018 (pp. 43-48). [8411679] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICHI-W.2018.00013

Characterizing Treatment Strategies of Intrahepatic Bile Duct Cancer by Literature Mining. / Guo, Hongmei; Zhang, Ning; Hong, Na; Yao, Lixia; Duan, Wenfei; Zhang, Zhixiong.

Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 43-48 8411679.

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

Guo, H, Zhang, N, Hong, N, Yao, L, Duan, W & Zhang, Z 2018, Characterizing Treatment Strategies of Intrahepatic Bile Duct Cancer by Literature Mining. in Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018., 8411679, Institute of Electrical and Electronics Engineers Inc., pp. 43-48, 6th IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018, New York, United States, 6/4/18. https://doi.org/10.1109/ICHI-W.2018.00013

@inproceedings{6ab912f7aaa04d1190ce8c2f5c0414bb,

title = "Characterizing Treatment Strategies of Intrahepatic Bile Duct Cancer by Literature Mining",

abstract = "Scientific literature is arguably the most objective, comprehensive and timely data source of medical knowledge discovery and evidence based medicine, as the significant clinical outcomes are often reported in a timely fashion in scientific journals. In this project, we proposed a text mining method for acquiring treatment strategies of intrahepatic bile duct cancer from PubMed abstracts. Treatment related concepts including therapeutic procedures, medications, genes and medical devices were systematically extracted and analyzed. A term co-occurrence network was generated to identify the main topics of the latest research on treatment of intrahepatic bile duct cancer. The treatment relations about 4 groups of different concepts, including Therapeutic Procedure, Medication, Care Activity, and Medical Device, were identified and demonstrated. Our study showed that the automatic treatment strategies extraction from literature with a small amount of physicians' verification added is a feasible solution to quickly characterize the overall treatment research progress.",

keywords = "iCCA, intrahepatic bile duct cancer, intrahepatic cholangiocarcinoma, relation extraction, text mining, treatment",

author = "Hongmei Guo and Ning Zhang and Na Hong and Lixia Yao and Wenfei Duan and Zhixiong Zhang",

year = "2018",

month = jul,

day = "16",

doi = "10.1109/ICHI-W.2018.00013",

language = "English (US)",

pages = "43--48",

booktitle = "Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "6th IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018 ; Conference date: 04-06-2018 Through 07-06-2018",

}

TY - GEN

T1 - Characterizing Treatment Strategies of Intrahepatic Bile Duct Cancer by Literature Mining

AU - Guo, Hongmei

AU - Zhang, Ning

AU - Hong, Na

AU - Yao, Lixia

AU - Duan, Wenfei

AU - Zhang, Zhixiong

PY - 2018/7/16

Y1 - 2018/7/16

N2 - Scientific literature is arguably the most objective, comprehensive and timely data source of medical knowledge discovery and evidence based medicine, as the significant clinical outcomes are often reported in a timely fashion in scientific journals. In this project, we proposed a text mining method for acquiring treatment strategies of intrahepatic bile duct cancer from PubMed abstracts. Treatment related concepts including therapeutic procedures, medications, genes and medical devices were systematically extracted and analyzed. A term co-occurrence network was generated to identify the main topics of the latest research on treatment of intrahepatic bile duct cancer. The treatment relations about 4 groups of different concepts, including Therapeutic Procedure, Medication, Care Activity, and Medical Device, were identified and demonstrated. Our study showed that the automatic treatment strategies extraction from literature with a small amount of physicians' verification added is a feasible solution to quickly characterize the overall treatment research progress.

AB - Scientific literature is arguably the most objective, comprehensive and timely data source of medical knowledge discovery and evidence based medicine, as the significant clinical outcomes are often reported in a timely fashion in scientific journals. In this project, we proposed a text mining method for acquiring treatment strategies of intrahepatic bile duct cancer from PubMed abstracts. Treatment related concepts including therapeutic procedures, medications, genes and medical devices were systematically extracted and analyzed. A term co-occurrence network was generated to identify the main topics of the latest research on treatment of intrahepatic bile duct cancer. The treatment relations about 4 groups of different concepts, including Therapeutic Procedure, Medication, Care Activity, and Medical Device, were identified and demonstrated. Our study showed that the automatic treatment strategies extraction from literature with a small amount of physicians' verification added is a feasible solution to quickly characterize the overall treatment research progress.

KW - iCCA

KW - intrahepatic bile duct cancer

KW - intrahepatic cholangiocarcinoma

KW - relation extraction

KW - text mining

KW - treatment

UR - http://www.scopus.com/inward/record.url?scp=85051012650&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85051012650&partnerID=8YFLogxK

U2 - 10.1109/ICHI-W.2018.00013

DO - 10.1109/ICHI-W.2018.00013

M3 - Conference contribution

AN - SCOPUS:85051012650

SP - 43

EP - 48

BT - Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018

Y2 - 4 June 2018 through 7 June 2018

ER -

Access to Document

10.1109/ICHI-W.2018.00013