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

Ling Chen, Xue Li, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations


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.

Original languageEnglish
Title of host publicationProceedings of the 24th Australasian Database Conference, ADC 2013
EditorsHua Wang, Rui Zhang
PublisherAustralian Computer Society
Number of pages10
ISBN (Electronic)9781921770227
StatePublished - Jan 2013
Event24th Australasian Database Conference, ADC 2013 - Adelaide, Australia
Duration: 29 Jan 20131 Feb 2013

Publication series

NameConferences in Research and Practice in Information Technology Series
ISSN (Print)1445-1336


Conference24th Australasian Database Conference, ADC 2013


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