MedRank: Discovering influential medical treatments from literature by information network analysis

Ling Chen, Xue Li, Jiawei Han

研究成果: Conference contribution同行評審

5 引文 斯高帕斯(Scopus)

摘要

Medical literature has been an important information source for clinical professionals. As the body of medical literature expands rapidly, keeping this knowledge up-to-date becomes a challenge for medical professionals. One question is that for a given disease how can we find the most influential treatments currently available from online medical publications? In this paper we propose MedRank, a new network-based algorithm that ranks heterogeneous objects in a medical information network. The network is extracted from MEDLINE, a large collection of semi-structured medical literature. Different types of objects such as journal articles, pathological symptoms, diseases, clinical trials, treatments, authors, and journals are linked together through their relationships. The experimental results are compared with the expert rankings collected from doctors and two baseline methods, namely degree centrality and NetClus. The evaluation shows that our algorithm is effective and efficient. The success of categorized entity ranking in medical literature domain suggests a new methodology and a potential success in ranking semi-structured data in other domains.

原文English
主出版物標題Proceedings of the 24th Australasian Database Conference, ADC 2013
編輯Hua Wang, Rui Zhang
發行者Australian Computer Society
頁面3-12
頁數10
ISBN(電子)9781921770227
出版狀態Published - 1月 2013
事件24th Australasian Database Conference, ADC 2013 - Adelaide, Australia
持續時間: 29 1月 20131 2月 2013

出版系列

名字Conferences in Research and Practice in Information Technology Series
137
ISSN(列印)1445-1336

Conference

Conference24th Australasian Database Conference, ADC 2013
國家/地區Australia
城市Adelaide
期間29/01/131/02/13

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