Publication Type

Journal Article

Version

publishedVersion

Publication Date

2-2015

Abstract

The vocabulary gap between health seekers and providers has hindered the cross-system operability and the interuser reusability. To bridge this gap, this paper presents a novel scheme to code the medical records by jointly utilizing local mining and global learning approaches, which are tightly linked and mutually reinforced. Local mining attempts to code the individual medical record by independently extracting the medical concepts from the medical record itself and then mapping them to authenticated terminologies. A corpus-aware terminology vocabulary is naturally constructed as a byproduct, which is used as the terminology space for global learning. Local mining approach, however, may suffer from information loss and lower precision, which are caused by the absence of key medical concepts and the presence of irrelevant medical concepts. Global learning, on the other hand, works towards enhancing the local medical coding via collaboratively discovering missing key terminologies and keeping off the irrelevant terminologies by analyzing the social neighbors. Comprehensive experiments well validate the proposed scheme and each of its component. Practically, this unsupervised scheme holds potential to large-scale data.

Keywords

Healthcare, global learning, local mining, medical terminology assignment, question answering

Discipline

Computer Sciences | Databases and Information Systems | Medicine and Health Sciences | Numerical Analysis and Scientific Computing

Publication

IEEE Transactions on Knowledge and Data Engineering (TKDE)

Volume

27

Issue

2

First Page

1041

Last Page

4347

ISSN

1041-4347

Identifier

10.1109/TKDE.2014.2330813

Publisher

IEEE

Copyright Owner and License

Authors

Additional URL

http://doi.org/10.1109/TKDE.2014.2330813

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