Title
HEMnet: Integration of Electronic Medical Records with Molecular Interaction Networks and Domain Knowledge for Survival Analysis
Abstract
The continual growth of electronic medical record (EMR) databases has paved the way for many data mining applications, including the discovery of novel disease-drug associations and the prediction of patient survival rates. However, these tasks are hindered because EMRs are usually segmented or incomplete. EMR analysis is further limited by the overabundance of medical term synonyms and morphologies, which causes existing techniques to mismatch records containing semantically similar but lexically distinct terms. Current solutions fill in missing values with techniques that tend to introduce noise rather than reduce it. In this paper, we propose to simultaneously infer missing data and solve semantic mismatching in EMRs by first integrating EMR data with molecular interaction networks and domain knowledge to build the HEMnet, a heterogeneous medical information network. We then project this network onto a low-dimensional space, and group entities in the network according to their relative distances. Lastly, we use this entity distance information to enrich the original EMRs. We evaluate the effectiveness of this method according to its ability to separate patients with dissimilar survival functions. We show that our method can obtain significant (p-value
Year
DOI
Venue
2017
10.1145/3107411.3107422
BCB
Field
DocType
ISBN
Data mining,Domain knowledge,Computer science,Medical record,Missing data,Network embedding,Bioinformatics
Conference
978-1-4503-4722-8
Citations 
PageRank 
References 
0
0.34
12
Authors
10
Name
Order
Citations
PageRank
Edward Huang122.39
Sheng Wang2498.26
Bingxue Li300.34
Ran Zhang400.34
Baoyan Liu521.38
Runshun Zhang6219.37
Jie Liu710543.72
Xuezhong Zhou820930.20
Hongsheng Lin900.34
ChengXiang Zhai1011908649.74