TY - GEN
T1 - An integrated web-based system for MEDLINE analysis
T2 - 22nd Pacific Asia Conference on Information Systems - Opportunities and Challenges for the Digitized Society: Are We Ready?, PACIS 2018
AU - Lin, Yi Ling
AU - Huang, Wei En
AU - Liang, Peir In
AU - Tung, Chun Wei
N1 - Funding Information:
This work was sponsored by National Sun Yat-sen University and Kaohsiung Medical University Project in Taiwan: NSYSUKMU 105-I005, and MOST 106-2410-H-004-081 to the first author.
Funding Information:
This work was sponsored by National SunYat -sen University and KaohsiunMgedca l iUniversity Project in Taiwan: NSYSUKMU 10-I0055, and MOST 106-2410-H-004-081 to the first auth. or
Publisher Copyright:
© PACIS 2018.
PY - 2018
Y1 - 2018
N2 - In the era of big data, medical researchers attempt to utilize some analysis techniques like machine learning and text mining on their large-scale corpora to save valuable labor work and time. Consequently, many data analysis platforms are built to support medical professionals such as Pubtator, GeneWays, BioContext, etc. These platforms are helpful to medical entities recognition and relation extraction, but there is not an integrated platform to support researchers' various needs, and medical projects are isolated from each other, which is hard to be shared and reused. As a result, we present an integrated system containing 'name entity recognition', 'document categorization' and 'association extraction'. Besides, we add the concept of 'socialization' making projects reusable for further analyses. A case study of chronic kidney disease was adopted to indicate the effectiveness of the proposed system.
AB - In the era of big data, medical researchers attempt to utilize some analysis techniques like machine learning and text mining on their large-scale corpora to save valuable labor work and time. Consequently, many data analysis platforms are built to support medical professionals such as Pubtator, GeneWays, BioContext, etc. These platforms are helpful to medical entities recognition and relation extraction, but there is not an integrated platform to support researchers' various needs, and medical projects are isolated from each other, which is hard to be shared and reused. As a result, we present an integrated system containing 'name entity recognition', 'document categorization' and 'association extraction'. Besides, we add the concept of 'socialization' making projects reusable for further analyses. A case study of chronic kidney disease was adopted to indicate the effectiveness of the proposed system.
KW - Association extraction
KW - Machine learning
KW - Medical analysis
KW - Name entity recognition
KW - Sharing
KW - Text mining
UR - http://www.scopus.com/inward/record.url?scp=85089237211&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85089237211&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85089237211
T3 - Proceedings of the 22nd Pacific Asia Conference on Information Systems - Opportunities and Challenges for the Digitized Society: Are We Ready?, PACIS 2018
BT - Proceedings of the 22nd Pacific Asia Conference on Information Systems - Opportunities and Challenges for the Digitized Society
A2 - Tanabu, Motonari
A2 - Senoo, Dai
PB - Association for Information Systems
Y2 - 26 June 2018 through 30 June 2018
ER -