A common type system for clinical natural language processing

DSpace/Manakin Repository

A common type system for clinical natural language processing

Citable link to this page

 

 
Title: A common type system for clinical natural language processing
Author: Wu, Stephen T; Kaggal, Vinod C; Dligach, Dmitriy; Masanz, James J; Chen, Pei; Becker, Lee; Chapman, Wendy W; Savova, Guergana Kirilova; Liu, Hongfang; Chute, Christopher G

Note: Order does not necessarily reflect citation order of authors.

Citation: Wu, Stephen T, Vinod C Kaggal, Dmitriy Dligach, James J Masanz, Pei Chen, Lee Becker, Wendy W Chapman, Guergana K Savova, Hongfang Liu, and Christopher G Chute. 2013. A common type system for clinical natural language processing. Journal of Biomedical Semantics 4: 1.
Full Text & Related Files:
Abstract: Background: One challenge in reusing clinical data stored in electronic medical records is that these data are heterogenous. Clinical Natural Language Processing (NLP) plays an important role in transforming information in clinical text to a standard representation that is comparable and interoperable. Information may be processed and shared when a type system specifies the allowable data structures. Therefore, we aim to define a common type system for clinical NLP that enables interoperability between structured and unstructured data generated in different clinical settings. Results: We describe a common type system for clinical NLP that has an end target of deep semantics based on Clinical Element Models (CEMs), thus interoperating with structured data and accommodating diverse NLP approaches. The type system has been implemented in UIMA (Unstructured Information Management Architecture) and is fully functional in a popular open-source clinical NLP system, cTAKES (clinical Text Analysis and Knowledge Extraction System) versions 2.0 and later. Conclusions: We have created a type system that targets deep semantics, thereby allowing for NLP systems to encapsulate knowledge from text and share it alongside heterogenous clinical data sources. Rather than surface semantics that are typically the end product of NLP algorithms, CEM-based semantics explicitly build in deep clinical semantics as the point of interoperability with more structured data types.
Published Version: doi:10.1186/2041-1480-4-1
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3575354/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:10589779
Downloads of this work:

Show full Dublin Core record

This item appears in the following Collection(s)

 
 

Search DASH


Advanced Search
 
 

Submitters