@inproceedings{ea083fab84914939b221041550c09f26,
title = "An integrated web-based system for MEDLINE analysis: A case study of chronic kidney disease",
abstract = "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.",
keywords = "Association extraction, Machine learning, Medical analysis, Name entity recognition, Sharing, Text mining",
author = "Lin, {Yi Ling} and Huang, {Wei En} and Liang, {Peir In} and Tung, {Chun Wei}",
note = "Publisher Copyright: {\textcopyright} PACIS 2018.; 22nd Pacific Asia Conference on Information Systems - Opportunities and Challenges for the Digitized Society: Are We Ready?, PACIS 2018 ; Conference date: 26-06-2018 Through 30-06-2018",
year = "2018",
language = "English",
series = "Proceedings of the 22nd Pacific Asia Conference on Information Systems - Opportunities and Challenges for the Digitized Society: Are We Ready?, PACIS 2018",
publisher = "Association for Information Systems",
editor = "Motonari Tanabu and Dai Senoo",
booktitle = "Proceedings of the 22nd Pacific Asia Conference on Information Systems - Opportunities and Challenges for the Digitized Society",
address = "United States",
}