A common type system for clinical natural language processing

Journal of Biomedical Semantics

Christopher G Chute¹  Hongfang Liu¹  Guergana K Savova⁴  Wendy W Chapman²  Lee Becker³  Pei Chen⁴  James J Masanz¹  Dmitriy Dligach⁴  Vinod C Kaggal¹  Stephen T Wu¹ 

[1] Mayo Clinic, Rochester, Rochester, MN, USA;University of California, San Diego, San Diego, CA, USA;University of Colorado at Boulder, Boulder, CO, USA;Childrens Hospital Boston and Harvard Medical School, Boston, MA, USA

关键词: Common type system;    Clinical Element Models;    Clinical information extraction;    Standards and interoperability;    Natural Language Processing;   

Others  :  812613
DOI  :  10.1186/2041-1480-4-1

 received in 2012-06-26, accepted in 2012-12-23,  发布年份 2013

【 摘 要 】

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.

【 授权许可】

   
2013 Wu et al.; licensee BioMed Central Ltd.

【 图 表 】

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